diff --git a/.github/workflows/ci-cd.yml b/.github/workflows/ci-cd.yml
new file mode 100644
index 000000000..cbf7ba57b
--- /dev/null
+++ b/.github/workflows/ci-cd.yml
@@ -0,0 +1,80 @@
+name: rushWash 학과 서버 배포 파이프라인
+
+on:
+  push:
+    branches: [ "main", "deploy-dev" ]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+
+      - name: Setup Gradle Cache
+        uses: actions/cache@v4
+        with:
+          path: |
+            ~/.gradle/caches
+            ~/.gradle/wrapper
+          key: ${{ runner.os }}-gradle-${{ hashFiles('**/*.gradle*', '**/gradle-wrapper.properties') }}
+          restore-keys: |
+            ${{ runner.os }}-gradle-
+
+      - name: JDK 17 설치
+        uses: actions/setup-java@v4
+        with:
+          java-version: '17'
+          distribution: 'temurin'
+
+      - name: frontend 로 front 파일 복사 (변경된 파일만)
+        run: |
+          rsync -a --delete front/fe-rw/ back/rushWash/src/main/frontend/
+
+      - name: Grant permission to Gradle
+        run: chmod +x back/rushWash/gradlew
+        working-directory: .
+        env:
+          CI: false
+
+      - name: application.yml 파일 설정
+        run: |
+          mkdir -p back/rushWash/src/main/resources
+          printf "%s" "${{ secrets.APPLICATION_YML }}" > back/rushWash/src/main/resources/application.yml
+
+      - name: Build jar
+        run: ./gradlew build -x test
+        working-directory: back/rushWash
+
+      - name: Setup SSH agent
+        uses: webfactory/ssh-agent@v0.5.4
+        with:
+          ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }}
+#       학과서버 닫음
+#      - name: 배포 back 디렉토리 만들기
+#        run: ssh -o StrictHostKeyChecking=no -p 22 t25119@ceprj.gachon.ac.kr "mkdir -p /home/t25119/v109.src/back"
+#
+#      - name: jar 학과 서버로 복사
+#        run: scp -o StrictHostKeyChecking=no -P 22 back/rushWash/build/libs/rushWash-0.0.1-SNAPSHOT.jar t25119@ceprj.gachon.ac.kr:/home/t25119/v109.src/back
+#
+#      - name: ai 폴더 동기화 (변경된 파일만)
+#        run: |
+#          rsync -avz --delete \
+#            -e "ssh -o StrictHostKeyChecking=no -p 22" \
+#            ai/ t25119@ceprj.gachon.ac.kr:/home/t25119/v109.src/ai/
+#
+#      - name: 서버 실행
+#        run: |
+#          ssh -o StrictHostKeyChecking=no -p 22 t25119@ceprj.gachon.ac.kr "
+#            if [ -f /home/t25119/v109.src/app.pid ]; then
+#              kill \$(cat /home/t25119/v109.src/app.pid) || true
+#            fi
+#
+#            # 🔹 test.py 실행 (서버 실행 전에)
+#            cd /home/t25119/v109.src/ai && python3 test.py
+#
+#            # 🔹 백엔드 실행
+#            nohup java -jar /home/t25119/v109.src/back/rushWash-0.0.1-SNAPSHOT.jar > /home/t25119/v109.src/app.log 2>&1 &
+#            echo \$! > /home/t25119/v109.src/app.pid
+#          "
diff --git a/.gitignore b/.gitignore
index c18c19a92..1c7c1b776 100644
--- a/.gitignore
+++ b/.gitignore
@@ -36,4 +36,11 @@ out/
 ### VS Code ###
 .vscode/
 *.yml
+!.github/workflows/*.yml
 
+### python ###
+venv
+stain_input
+symbol_input
+llm
+runs
\ No newline at end of file
diff --git a/ai/data/stain/data.yaml b/ai/data/stain/data.yaml
new file mode 100644
index 000000000..37ca60f78
--- /dev/null
+++ b/ai/data/stain/data.yaml
@@ -0,0 +1,6 @@
+train: /home/t25119/v109.src/ai/data/stain/train/images
+val: /home/t25119/v109.src/ai/data/stain/val/images
+test: /home/t25119/v109.src/ai/data/stain/test/images
+
+nc: 9
+names: [blood, coffee, earth, ink, kimchi, lipstick, mustard, oil, wine]
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index 000000000..e05102e11
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index 000000000..dc5d40af2
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index 000000000..1caa340ca
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index 000000000..0034fd533
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diff --git a/ai/data/stain/val/labels/blood_005.txt b/ai/data/stain/val/labels/blood_005.txt
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index 000000000..9a617ce88
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_005.txt
@@ -0,0 +1 @@
+0 0.446094 0.534375 0.545312 0.553125
diff --git a/ai/data/stain/val/labels/blood_008.txt b/ai/data/stain/val/labels/blood_008.txt
new file mode 100644
index 000000000..ce92fa6a5
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_008.txt
@@ -0,0 +1,4 @@
+0 0.600781 0.550000 0.264062 0.384375
+0 0.303125 0.544531 0.250000 0.239063
+0 0.728125 0.037500 0.075000 0.065625
+0 0.166406 0.765625 0.132812 0.121875
diff --git a/ai/data/stain/val/labels/blood_013.txt b/ai/data/stain/val/labels/blood_013.txt
new file mode 100644
index 000000000..b45937a18
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_013.txt
@@ -0,0 +1,5 @@
+0 0.414844 0.459375 0.064062 0.146875
+0 0.520312 0.294531 0.065625 0.064062
+0 0.525781 0.389062 0.042188 0.062500
+0 0.576562 0.240625 0.021875 0.046875
+0 0.528906 0.171094 0.026562 0.039062
diff --git a/ai/data/stain/val/labels/blood_027.txt b/ai/data/stain/val/labels/blood_027.txt
new file mode 100644
index 000000000..88cb2b975
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_027.txt
@@ -0,0 +1,4 @@
+0 0.540000 0.538251 0.076364 0.136612
+0 0.454545 0.707650 0.072727 0.147541
+0 0.463636 0.565574 0.047273 0.114754
+0 0.240000 0.284153 0.043636 0.032787
diff --git a/ai/data/stain/val/labels/blood_029.txt b/ai/data/stain/val/labels/blood_029.txt
new file mode 100644
index 000000000..85643ed55
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_029.txt
@@ -0,0 +1,2 @@
+0 0.343511 0.489583 0.244275 0.281250
+0 0.656489 0.382812 0.282443 0.348958
diff --git a/ai/data/stain/val/labels/blood_034.txt b/ai/data/stain/val/labels/blood_034.txt
new file mode 100644
index 000000000..2a1bae876
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_034.txt
@@ -0,0 +1,3 @@
+0 0.593156 0.486979 0.076046 0.078125
+0 0.486692 0.458333 0.091255 0.125000
+0 0.602662 0.653646 0.087452 0.057292
diff --git a/ai/data/stain/val/labels/blood_036.txt b/ai/data/stain/val/labels/blood_036.txt
new file mode 100644
index 000000000..49351c4b9
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_036.txt
@@ -0,0 +1 @@
+0 0.637405 0.326425 0.274809 0.279793
diff --git a/ai/data/stain/val/labels/blood_048.txt b/ai/data/stain/val/labels/blood_048.txt
new file mode 100644
index 000000000..6c46c4d4f
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_048.txt
@@ -0,0 +1 @@
+0 0.566794 0.455729 0.270992 0.338542
diff --git a/ai/data/stain/val/labels/blood_050.txt b/ai/data/stain/val/labels/blood_050.txt
new file mode 100644
index 000000000..3fb369156
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_050.txt
@@ -0,0 +1,3 @@
+0 0.495000 0.351190 0.120000 0.075397
+0 0.407500 0.263889 0.045000 0.035714
+0 0.432500 0.708333 0.095000 0.075397
diff --git a/ai/data/stain/val/labels/blood_052.txt b/ai/data/stain/val/labels/blood_052.txt
new file mode 100644
index 000000000..5d0249ee0
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_052.txt
@@ -0,0 +1 @@
+0 0.498333 0.577381 0.123333 0.250000
diff --git a/ai/data/stain/val/labels/blood_061.txt b/ai/data/stain/val/labels/blood_061.txt
new file mode 100644
index 000000000..594747de8
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_061.txt
@@ -0,0 +1 @@
+0 0.450909 0.486339 0.727273 0.655738
diff --git a/ai/data/stain/val/labels/blood_069.txt b/ai/data/stain/val/labels/blood_069.txt
new file mode 100644
index 000000000..338999d9c
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_069.txt
@@ -0,0 +1 @@
+0 0.477178 0.413876 0.091286 0.081340
diff --git a/ai/data/stain/val/labels/blood_071.txt b/ai/data/stain/val/labels/blood_071.txt
new file mode 100644
index 000000000..96e068191
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_071.txt
@@ -0,0 +1,6 @@
+0 0.862069 0.367816 0.151724 0.252874
+0 0.817241 0.876437 0.220690 0.247126
+0 0.375862 0.583333 0.641379 0.649425
+0 0.610345 0.045977 0.151724 0.080460
+0 0.977586 0.301724 0.044828 0.120690
+0 0.818966 0.551724 0.113793 0.080460
diff --git a/ai/data/stain/val/labels/blood_075.txt b/ai/data/stain/val/labels/blood_075.txt
new file mode 100644
index 000000000..098e8feab
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_075.txt
@@ -0,0 +1,3 @@
+0 0.692460 0.750000 0.059524 0.060000
+0 0.845238 0.487500 0.039683 0.065000
+0 0.779762 0.575000 0.043651 0.090000
diff --git a/ai/data/stain/val/labels/blood_077.txt b/ai/data/stain/val/labels/blood_077.txt
new file mode 100644
index 000000000..6488a6fc3
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_077.txt
@@ -0,0 +1,3 @@
+0 0.346021 0.480000 0.145329 0.137143
+0 0.553633 0.602857 0.089965 0.108571
+0 0.638408 0.394286 0.058824 0.091429
diff --git a/ai/data/stain/val/labels/blood_084.txt b/ai/data/stain/val/labels/blood_084.txt
new file mode 100644
index 000000000..4df49b89f
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_084.txt
@@ -0,0 +1,4 @@
+0 0.232727 0.491803 0.087273 0.153005
+0 0.396364 0.513661 0.123636 0.153005
+0 0.340000 0.685792 0.120000 0.169399
+0 0.214545 0.721311 0.101818 0.120219
diff --git a/ai/data/stain/val/labels/blood_085.txt b/ai/data/stain/val/labels/blood_085.txt
new file mode 100644
index 000000000..358c2cd15
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_085.txt
@@ -0,0 +1,2 @@
+0 0.547273 0.497268 0.090909 0.262295
+0 0.518182 0.825137 0.134545 0.338798
diff --git a/ai/data/stain/val/labels/blood_093.txt b/ai/data/stain/val/labels/blood_093.txt
new file mode 100644
index 000000000..cc5368f41
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_093.txt
@@ -0,0 +1,4 @@
+0 0.432990 0.498069 0.051546 0.123552
+0 0.543814 0.306950 0.067010 0.034749
+0 0.670103 0.250965 0.072165 0.061776
+0 0.726804 0.324324 0.051546 0.038610
diff --git a/ai/data/stain/val/labels/blood_097.txt b/ai/data/stain/val/labels/blood_097.txt
new file mode 100644
index 000000000..b083d6e76
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_097.txt
@@ -0,0 +1,3 @@
+0 0.540000 0.560109 0.156364 0.224044
+0 0.743636 0.565574 0.170909 0.497268
+0 0.796364 0.254098 0.072727 0.125683
diff --git a/ai/data/stain/val/labels/blood_099.txt b/ai/data/stain/val/labels/blood_099.txt
new file mode 100644
index 000000000..649dc6f05
--- /dev/null
+++ b/ai/data/stain/val/labels/blood_099.txt
@@ -0,0 +1,2 @@
+0 0.417526 0.306950 0.268041 0.235521
+0 0.615979 0.548263 0.283505 0.223938
diff --git a/ai/data/stain/val/labels/coffee_001.txt b/ai/data/stain/val/labels/coffee_001.txt
new file mode 100644
index 000000000..b7acbad9c
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_001.txt
@@ -0,0 +1 @@
+1 0.282609 0.538690 0.150502 0.363095
diff --git a/ai/data/stain/val/labels/coffee_005.txt b/ai/data/stain/val/labels/coffee_005.txt
new file mode 100644
index 000000000..bd542fb86
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_005.txt
@@ -0,0 +1 @@
+1 0.195423 0.646893 0.214789 0.310734
diff --git a/ai/data/stain/val/labels/coffee_010.txt b/ai/data/stain/val/labels/coffee_010.txt
new file mode 100644
index 000000000..2c9acdbf8
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_010.txt
@@ -0,0 +1 @@
+1 0.669091 0.532787 0.254545 0.879781
diff --git a/ai/data/stain/val/labels/coffee_011.txt b/ai/data/stain/val/labels/coffee_011.txt
new file mode 100644
index 000000000..d059c24b8
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_011.txt
@@ -0,0 +1 @@
+1 0.554054 0.365639 0.108108 0.123348
diff --git a/ai/data/stain/val/labels/coffee_019.txt b/ai/data/stain/val/labels/coffee_019.txt
new file mode 100644
index 000000000..552b7a320
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_019.txt
@@ -0,0 +1 @@
+1 0.091525 0.163743 0.074576 0.233918
diff --git a/ai/data/stain/val/labels/coffee_021.txt b/ai/data/stain/val/labels/coffee_021.txt
new file mode 100644
index 000000000..dbb113b95
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_021.txt
@@ -0,0 +1 @@
+1 0.330909 0.215847 0.065455 0.213115
diff --git a/ai/data/stain/val/labels/coffee_024.txt b/ai/data/stain/val/labels/coffee_024.txt
new file mode 100644
index 000000000..1fbb1d488
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_024.txt
@@ -0,0 +1,2 @@
+1 0.543750 0.631250 0.062500 0.100000
+1 0.450000 0.691406 0.096875 0.110937
diff --git a/ai/data/stain/val/labels/coffee_035.txt b/ai/data/stain/val/labels/coffee_035.txt
new file mode 100644
index 000000000..33a32f482
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_035.txt
@@ -0,0 +1,4 @@
+1 0.822115 0.206731 0.110577 0.149038
+1 0.655048 0.259615 0.117788 0.216346
+1 0.789663 0.677885 0.161058 0.096154
+1 0.901442 0.532452 0.062500 0.209135
diff --git a/ai/data/stain/val/labels/coffee_050.txt b/ai/data/stain/val/labels/coffee_050.txt
new file mode 100644
index 000000000..fc23e5c9b
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_050.txt
@@ -0,0 +1,2 @@
+1 0.632212 0.645433 0.274038 0.242788
+1 0.758413 0.456731 0.209135 0.105769
diff --git a/ai/data/stain/val/labels/coffee_051.txt b/ai/data/stain/val/labels/coffee_051.txt
new file mode 100644
index 000000000..bc4e596b8
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_051.txt
@@ -0,0 +1,2 @@
+1 0.840144 0.754808 0.185096 0.134615
+1 0.804087 0.890625 0.112981 0.112981
diff --git a/ai/data/stain/val/labels/coffee_055.txt b/ai/data/stain/val/labels/coffee_055.txt
new file mode 100644
index 000000000..ced5e1f09
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_055.txt
@@ -0,0 +1 @@
+1 0.768029 0.370192 0.079327 0.125000
diff --git a/ai/data/stain/val/labels/coffee_067.txt b/ai/data/stain/val/labels/coffee_067.txt
new file mode 100644
index 000000000..7bb3cce1a
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_067.txt
@@ -0,0 +1,3 @@
+1 0.753606 0.418269 0.218750 0.115385
+1 0.635817 0.313702 0.060096 0.074519
+1 0.721154 0.520433 0.100962 0.064904
diff --git a/ai/data/stain/val/labels/coffee_070.txt b/ai/data/stain/val/labels/coffee_070.txt
new file mode 100644
index 000000000..1552e3645
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_070.txt
@@ -0,0 +1,3 @@
+1 0.475962 0.378606 0.144231 0.156250
+1 0.508413 0.593750 0.151442 0.129808
+1 0.461538 0.813702 0.100962 0.084135
diff --git a/ai/data/stain/val/labels/coffee_072.txt b/ai/data/stain/val/labels/coffee_072.txt
new file mode 100644
index 000000000..545b7370e
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_072.txt
@@ -0,0 +1 @@
+1 0.651442 0.718750 0.100962 0.091346
diff --git a/ai/data/stain/val/labels/coffee_075.txt b/ai/data/stain/val/labels/coffee_075.txt
new file mode 100644
index 000000000..cca24f65b
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_075.txt
@@ -0,0 +1,7 @@
+1 0.558894 0.510817 0.040865 0.069712
+1 0.622596 0.389423 0.048077 0.163462
+1 0.545673 0.342548 0.024038 0.026442
+1 0.362981 0.355769 0.038462 0.024038
+1 0.324519 0.554087 0.024038 0.031250
+1 0.325721 0.685096 0.026442 0.028846
+1 0.388221 0.259615 0.060096 0.052885
diff --git a/ai/data/stain/val/labels/coffee_078.txt b/ai/data/stain/val/labels/coffee_078.txt
new file mode 100644
index 000000000..58c4a26cc
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_078.txt
@@ -0,0 +1 @@
+1 0.656250 0.395433 0.182692 0.194712
diff --git a/ai/data/stain/val/labels/coffee_079.txt b/ai/data/stain/val/labels/coffee_079.txt
new file mode 100644
index 000000000..263f8bb94
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_079.txt
@@ -0,0 +1,2 @@
+1 0.602163 0.290865 0.084135 0.158654
+1 0.480769 0.342548 0.057692 0.112981
diff --git a/ai/data/stain/val/labels/coffee_087.txt b/ai/data/stain/val/labels/coffee_087.txt
new file mode 100644
index 000000000..e9bee8d65
--- /dev/null
+++ b/ai/data/stain/val/labels/coffee_087.txt
@@ -0,0 +1,2 @@
+1 0.580529 0.772837 0.262019 0.132212
+1 0.912260 0.544471 0.098558 0.074519
diff --git a/ai/data/stain/val/labels/earth_109.txt b/ai/data/stain/val/labels/earth_109.txt
new file mode 100644
index 000000000..8d7300553
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_109.txt
@@ -0,0 +1,4 @@
+2 0.408879 0.625000 0.079439 0.131356
+2 0.621495 0.622881 0.074766 0.127119
+2 0.338785 0.489407 0.070093 0.122881
+2 0.738318 0.512712 0.037383 0.076271
diff --git a/ai/data/stain/val/labels/earth_115.txt b/ai/data/stain/val/labels/earth_115.txt
new file mode 100644
index 000000000..0875b40bb
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_115.txt
@@ -0,0 +1,4 @@
+2 0.708763 0.291506 0.139175 0.096525
+2 0.884021 0.418919 0.097938 0.166023
+2 0.595361 0.555985 0.108247 0.077220
+2 0.530928 0.461390 0.061856 0.073359
diff --git a/ai/data/stain/val/labels/earth_125.txt b/ai/data/stain/val/labels/earth_125.txt
new file mode 100644
index 000000000..0337a902d
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_125.txt
@@ -0,0 +1,2 @@
+2 0.427273 0.513661 0.301818 0.218579
+2 0.343636 0.677596 0.105455 0.087432
diff --git a/ai/data/stain/val/labels/earth_127.txt b/ai/data/stain/val/labels/earth_127.txt
new file mode 100644
index 000000000..5ef9c0b95
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_127.txt
@@ -0,0 +1,3 @@
+2 0.429260 0.543210 0.073955 0.308642
+2 0.665595 0.614198 0.070740 0.277778
+2 0.350482 0.509259 0.025723 0.104938
diff --git a/ai/data/stain/val/labels/earth_128.txt b/ai/data/stain/val/labels/earth_128.txt
new file mode 100644
index 000000000..195e82a91
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_128.txt
@@ -0,0 +1,2 @@
+2 0.738095 0.545000 0.095238 0.083333
+2 0.279762 0.523333 0.083333 0.080000
diff --git a/ai/data/stain/val/labels/earth_130.txt b/ai/data/stain/val/labels/earth_130.txt
new file mode 100644
index 000000000..ddb7a6805
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_130.txt
@@ -0,0 +1,2 @@
+2 0.240000 0.593333 0.222222 0.191111
+2 0.777778 0.480000 0.231111 0.364444
diff --git a/ai/data/stain/val/labels/earth_133.txt b/ai/data/stain/val/labels/earth_133.txt
new file mode 100644
index 000000000..208f9a658
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_133.txt
@@ -0,0 +1,7 @@
+2 0.536260 0.791667 0.103053 0.156250
+2 0.509542 0.914062 0.087786 0.067708
+2 0.698473 0.533854 0.083969 0.109375
+2 0.458015 0.778646 0.030534 0.067708
+2 0.425573 0.830729 0.034351 0.046875
+2 0.633588 0.979167 0.045802 0.041667
+2 0.204198 0.583333 0.072519 0.062500
diff --git a/ai/data/stain/val/labels/earth_134.txt b/ai/data/stain/val/labels/earth_134.txt
new file mode 100644
index 000000000..26e2d99fe
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_134.txt
@@ -0,0 +1,2 @@
+2 0.341818 0.896175 0.116364 0.207650
+2 0.472727 0.945355 0.036364 0.098361
diff --git a/ai/data/stain/val/labels/earth_135.txt b/ai/data/stain/val/labels/earth_135.txt
new file mode 100644
index 000000000..9c66113b5
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_135.txt
@@ -0,0 +1,2 @@
+2 0.416667 0.398810 0.073333 0.369048
+2 0.661667 0.473214 0.063333 0.208333
diff --git a/ai/data/stain/val/labels/earth_138.txt b/ai/data/stain/val/labels/earth_138.txt
new file mode 100644
index 000000000..c5c13aab4
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_138.txt
@@ -0,0 +1,5 @@
+2 0.330909 0.057377 0.116364 0.103825
+2 0.532727 0.062842 0.112727 0.114754
+2 0.641818 0.155738 0.069091 0.125683
+2 0.698182 0.584699 0.050909 0.065574
+2 0.214545 0.857923 0.240000 0.218579
diff --git a/ai/data/stain/val/labels/earth_142.txt b/ai/data/stain/val/labels/earth_142.txt
new file mode 100644
index 000000000..8a6e1b4f0
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_142.txt
@@ -0,0 +1 @@
+2 0.505618 0.743386 0.284644 0.386243
diff --git a/ai/data/stain/val/labels/earth_143.txt b/ai/data/stain/val/labels/earth_143.txt
new file mode 100644
index 000000000..25c5dfe25
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_143.txt
@@ -0,0 +1 @@
+2 0.503636 0.538251 0.134545 0.224044
diff --git a/ai/data/stain/val/labels/earth_147.txt b/ai/data/stain/val/labels/earth_147.txt
new file mode 100644
index 000000000..bca96fef3
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_147.txt
@@ -0,0 +1 @@
+2 0.698997 0.205357 0.086957 0.184524
diff --git a/ai/data/stain/val/labels/earth_19.txt b/ai/data/stain/val/labels/earth_19.txt
new file mode 100644
index 000000000..86de398fb
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_19.txt
@@ -0,0 +1 @@
+2 0.488679 0.384211 0.147170 0.284211
diff --git a/ai/data/stain/val/labels/earth_20.txt b/ai/data/stain/val/labels/earth_20.txt
new file mode 100644
index 000000000..84258d80f
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_20.txt
@@ -0,0 +1 @@
+2 0.666023 0.471649 0.088803 0.139175
diff --git a/ai/data/stain/val/labels/earth_25.txt b/ai/data/stain/val/labels/earth_25.txt
new file mode 100644
index 000000000..69b1865d9
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_25.txt
@@ -0,0 +1,2 @@
+2 0.478333 0.574405 0.056667 0.148810
+2 0.325000 0.607143 0.070000 0.154762
diff --git a/ai/data/stain/val/labels/earth_36.txt b/ai/data/stain/val/labels/earth_36.txt
new file mode 100644
index 000000000..881d478e9
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_36.txt
@@ -0,0 +1,2 @@
+2 0.352830 0.368421 0.056604 0.189474
+2 0.743396 0.534211 0.075472 0.173684
diff --git a/ai/data/stain/val/labels/earth_37.txt b/ai/data/stain/val/labels/earth_37.txt
new file mode 100644
index 000000000..f08cc9982
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_37.txt
@@ -0,0 +1,4 @@
+2 0.366667 0.542411 0.040000 0.049107
+2 0.502222 0.571429 0.017778 0.044643
+2 0.477778 0.616071 0.031111 0.026786
+2 0.542222 0.580357 0.026667 0.026786
diff --git a/ai/data/stain/val/labels/earth_38.txt b/ai/data/stain/val/labels/earth_38.txt
new file mode 100644
index 000000000..c76db3c4c
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_38.txt
@@ -0,0 +1,6 @@
+2 0.183636 0.357923 0.156364 0.224044
+2 0.563636 0.871585 0.320000 0.256831
+2 0.925455 0.267760 0.120000 0.196721
+2 0.785455 0.442623 0.072727 0.262295
+2 0.660000 0.065574 0.105455 0.120219
+2 0.534545 0.267760 0.174545 0.174863
diff --git a/ai/data/stain/val/labels/earth_4.txt b/ai/data/stain/val/labels/earth_4.txt
new file mode 100644
index 000000000..ca2ce4ad8
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_4.txt
@@ -0,0 +1,2 @@
+2 0.670968 0.496933 0.103226 0.134969
+2 0.874194 0.582822 0.083871 0.171779
diff --git a/ai/data/stain/val/labels/earth_40.txt b/ai/data/stain/val/labels/earth_40.txt
new file mode 100644
index 000000000..1097165a5
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_40.txt
@@ -0,0 +1,2 @@
+2 0.590909 0.379781 0.069091 0.125683
+2 0.101818 0.636612 0.065455 0.158470
diff --git a/ai/data/stain/val/labels/earth_42.txt b/ai/data/stain/val/labels/earth_42.txt
new file mode 100644
index 000000000..8f2836417
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_42.txt
@@ -0,0 +1 @@
+2 0.503448 0.511494 0.131034 0.310345
diff --git a/ai/data/stain/val/labels/earth_47.txt b/ai/data/stain/val/labels/earth_47.txt
new file mode 100644
index 000000000..77d4010db
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_47.txt
@@ -0,0 +1,2 @@
+2 0.420755 0.194737 0.056604 0.126316
+2 0.773585 0.155263 0.045283 0.100000
diff --git a/ai/data/stain/val/labels/earth_5.txt b/ai/data/stain/val/labels/earth_5.txt
new file mode 100644
index 000000000..f9ec9be66
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_5.txt
@@ -0,0 +1 @@
+2 0.604247 0.530928 0.212355 0.309278
diff --git a/ai/data/stain/val/labels/earth_60.txt b/ai/data/stain/val/labels/earth_60.txt
new file mode 100644
index 000000000..8764c8b44
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_60.txt
@@ -0,0 +1,2 @@
+2 0.505000 0.544643 0.063333 0.101190
+2 0.400000 0.491071 0.046667 0.065476
diff --git a/ai/data/stain/val/labels/earth_64.txt b/ai/data/stain/val/labels/earth_64.txt
new file mode 100644
index 000000000..a0ec2a450
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_64.txt
@@ -0,0 +1,2 @@
+2 0.200000 0.666667 0.046667 0.107143
+2 0.770000 0.836310 0.073333 0.136905
diff --git a/ai/data/stain/val/labels/earth_84.txt b/ai/data/stain/val/labels/earth_84.txt
new file mode 100644
index 000000000..db0d0ba4e
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_84.txt
@@ -0,0 +1 @@
+2 0.518333 0.422619 0.223333 0.226190
diff --git a/ai/data/stain/val/labels/earth_85.txt b/ai/data/stain/val/labels/earth_85.txt
new file mode 100644
index 000000000..9a570f3f5
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_85.txt
@@ -0,0 +1,5 @@
+2 0.271795 0.488372 0.112821 0.116279
+2 0.258974 0.116279 0.117949 0.062016
+2 0.612821 0.255814 0.087179 0.062016
+2 0.671795 0.449612 0.102564 0.054264
+2 0.266667 0.286822 0.071795 0.077519
diff --git a/ai/data/stain/val/labels/earth_93.txt b/ai/data/stain/val/labels/earth_93.txt
new file mode 100644
index 000000000..80762832b
--- /dev/null
+++ b/ai/data/stain/val/labels/earth_93.txt
@@ -0,0 +1,3 @@
+2 0.656364 0.551913 0.207273 0.327869
+2 0.481818 0.625683 0.112727 0.333333
+2 0.358182 0.658470 0.105455 0.147541
diff --git a/ai/data/stain/val/labels/ink_008.txt b/ai/data/stain/val/labels/ink_008.txt
new file mode 100644
index 000000000..836dbe9f5
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_008.txt
@@ -0,0 +1 @@
+3 0.514706 0.612903 0.080882 0.182796
diff --git a/ai/data/stain/val/labels/ink_013.txt b/ai/data/stain/val/labels/ink_013.txt
new file mode 100644
index 000000000..5b2627cc1
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_013.txt
@@ -0,0 +1 @@
+3 0.285455 0.352459 0.083636 0.092896
diff --git a/ai/data/stain/val/labels/ink_018.txt b/ai/data/stain/val/labels/ink_018.txt
new file mode 100644
index 000000000..e3ada1850
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_018.txt
@@ -0,0 +1 @@
+3 0.748188 0.434426 0.228261 0.278689
diff --git a/ai/data/stain/val/labels/ink_033.txt b/ai/data/stain/val/labels/ink_033.txt
new file mode 100644
index 000000000..d96b1564f
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_033.txt
@@ -0,0 +1 @@
+3 0.478448 0.344828 0.071839 0.151724
diff --git a/ai/data/stain/val/labels/ink_037.txt b/ai/data/stain/val/labels/ink_037.txt
new file mode 100644
index 000000000..0e35d4e02
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_037.txt
@@ -0,0 +1 @@
+3 0.662116 0.462209 0.129693 0.156977
diff --git a/ai/data/stain/val/labels/ink_041.txt b/ai/data/stain/val/labels/ink_041.txt
new file mode 100644
index 000000000..1ada053a6
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_041.txt
@@ -0,0 +1 @@
+3 0.721992 0.461722 0.024896 0.033493
diff --git a/ai/data/stain/val/labels/ink_044.txt b/ai/data/stain/val/labels/ink_044.txt
new file mode 100644
index 000000000..8496d8d53
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_044.txt
@@ -0,0 +1 @@
+3 0.464883 0.824405 0.073579 0.184524
diff --git a/ai/data/stain/val/labels/ink_052.txt b/ai/data/stain/val/labels/ink_052.txt
new file mode 100644
index 000000000..2658073e7
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_052.txt
@@ -0,0 +1 @@
+3 0.482692 0.837629 0.157692 0.077320
diff --git a/ai/data/stain/val/labels/ink_061.txt b/ai/data/stain/val/labels/ink_061.txt
new file mode 100644
index 000000000..0853a3570
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_061.txt
@@ -0,0 +1 @@
+3 0.636364 0.631148 0.210909 0.311475
diff --git a/ai/data/stain/val/labels/ink_062.txt b/ai/data/stain/val/labels/ink_062.txt
new file mode 100644
index 000000000..22cf2f2a9
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_062.txt
@@ -0,0 +1 @@
+3 0.460000 0.464481 0.120000 0.131148
diff --git a/ai/data/stain/val/labels/ink_063.txt b/ai/data/stain/val/labels/ink_063.txt
new file mode 100644
index 000000000..74c6a05fc
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_063.txt
@@ -0,0 +1 @@
+3 0.742222 0.828889 0.133333 0.128889
diff --git a/ai/data/stain/val/labels/ink_065.txt b/ai/data/stain/val/labels/ink_065.txt
new file mode 100644
index 000000000..da663a9ed
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_065.txt
@@ -0,0 +1 @@
+3 0.529963 0.441489 0.123596 0.138298
diff --git a/ai/data/stain/val/labels/ink_069.txt b/ai/data/stain/val/labels/ink_069.txt
new file mode 100644
index 000000000..3379b646b
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_069.txt
@@ -0,0 +1 @@
+3 0.490909 0.546448 0.058182 0.076503
diff --git a/ai/data/stain/val/labels/ink_083.txt b/ai/data/stain/val/labels/ink_083.txt
new file mode 100644
index 000000000..105b3ce09
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_083.txt
@@ -0,0 +1,2 @@
+3 0.610825 0.447876 0.149485 0.092664
+3 0.590206 0.577220 0.149485 0.142857
diff --git a/ai/data/stain/val/labels/ink_086.txt b/ai/data/stain/val/labels/ink_086.txt
new file mode 100644
index 000000000..22d222b8b
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_086.txt
@@ -0,0 +1,7 @@
+3 0.536082 0.362934 0.061856 0.038610
+3 0.603093 0.416988 0.041237 0.030888
+3 0.592784 0.619691 0.092784 0.111969
+3 0.530928 0.779923 0.051546 0.038610
+3 0.515464 0.861004 0.041237 0.054054
+3 0.414948 0.291506 0.077320 0.027027
+3 0.559278 0.293436 0.046392 0.030888
diff --git a/ai/data/stain/val/labels/ink_088.txt b/ai/data/stain/val/labels/ink_088.txt
new file mode 100644
index 000000000..639eb16d9
--- /dev/null
+++ b/ai/data/stain/val/labels/ink_088.txt
@@ -0,0 +1,3 @@
+3 0.567010 0.438224 0.041237 0.034749
+3 0.551546 0.625483 0.061856 0.038610
+3 0.525773 0.777992 0.103093 0.065637
diff --git a/ai/data/stain/val/labels/kimchi_003.txt b/ai/data/stain/val/labels/kimchi_003.txt
new file mode 100644
index 000000000..441b53d2e
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_003.txt
@@ -0,0 +1,9 @@
+4 0.216406 0.124219 0.032813 0.032813
+4 0.289062 0.312500 0.028125 0.021875
+4 0.521094 0.207813 0.026562 0.028125
+4 0.521875 0.287500 0.028125 0.034375
+4 0.648438 0.393750 0.059375 0.065625
+4 0.614062 0.489844 0.100000 0.092188
+4 0.328906 0.643750 0.307812 0.312500
+4 0.259375 0.886719 0.056250 0.035937
+4 0.362500 0.872656 0.062500 0.042188
diff --git a/ai/data/stain/val/labels/kimchi_009.txt b/ai/data/stain/val/labels/kimchi_009.txt
new file mode 100644
index 000000000..b75d0a4af
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_009.txt
@@ -0,0 +1,2 @@
+4 0.451562 0.432031 0.262500 0.242188
+4 0.514062 0.615625 0.106250 0.078125
diff --git a/ai/data/stain/val/labels/kimchi_013.txt b/ai/data/stain/val/labels/kimchi_013.txt
new file mode 100644
index 000000000..c255e374e
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_013.txt
@@ -0,0 +1,4 @@
+4 0.417969 0.293750 0.129688 0.090625
+4 0.463281 0.430469 0.204687 0.151562
+4 0.268750 0.419531 0.125000 0.064062
+4 0.701562 0.614062 0.056250 0.062500
diff --git a/ai/data/stain/val/labels/kimchi_033.txt b/ai/data/stain/val/labels/kimchi_033.txt
new file mode 100644
index 000000000..eaca11d51
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_033.txt
@@ -0,0 +1,9 @@
+4 0.442308 0.360577 0.091346 0.076923
+4 0.588942 0.451923 0.115385 0.139423
+4 0.599760 0.562500 0.050481 0.052885
+4 0.467548 0.848558 0.156250 0.139423
+4 0.284856 0.843750 0.055288 0.043269
+4 0.443510 0.524038 0.170673 0.168269
+4 0.760817 0.500000 0.079327 0.062500
+4 0.731971 0.552885 0.117788 0.062500
+4 0.689904 0.604567 0.024038 0.026442
diff --git a/ai/data/stain/val/labels/kimchi_038.txt b/ai/data/stain/val/labels/kimchi_038.txt
new file mode 100644
index 000000000..eea2b27a7
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_038.txt
@@ -0,0 +1,3 @@
+4 0.331731 0.483173 0.062500 0.076923
+4 0.322115 0.600962 0.038462 0.043269
+4 0.631010 0.677885 0.223558 0.346154
diff --git a/ai/data/stain/val/labels/kimchi_041.txt b/ai/data/stain/val/labels/kimchi_041.txt
new file mode 100644
index 000000000..20aa182f6
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_041.txt
@@ -0,0 +1,7 @@
+4 0.632212 0.352163 0.052885 0.040865
+4 0.551683 0.408654 0.050481 0.043269
+4 0.460337 0.485577 0.098558 0.115385
+4 0.629808 0.562500 0.096154 0.076923
+4 0.861779 0.447115 0.098558 0.096154
+4 0.824519 0.524038 0.086538 0.062500
+4 0.522837 0.368990 0.093750 0.026442
diff --git a/ai/data/stain/val/labels/kimchi_043.txt b/ai/data/stain/val/labels/kimchi_043.txt
new file mode 100644
index 000000000..072f1d327
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_043.txt
@@ -0,0 +1 @@
+4 0.530048 0.426683 0.069712 0.064904
diff --git a/ai/data/stain/val/labels/kimchi_050.txt b/ai/data/stain/val/labels/kimchi_050.txt
new file mode 100644
index 000000000..125d9aa33
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_050.txt
@@ -0,0 +1,3 @@
+4 0.555288 0.510817 0.225962 0.213942
+4 0.537260 0.784856 0.026442 0.026442
+4 0.555288 0.510817 0.307692 0.382212
diff --git a/ai/data/stain/val/labels/kimchi_057.txt b/ai/data/stain/val/labels/kimchi_057.txt
new file mode 100644
index 000000000..e469339ad
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_057.txt
@@ -0,0 +1,3 @@
+4 0.584135 0.453125 0.197115 0.189904
+4 0.475962 0.578125 0.057692 0.093750
+4 0.472356 0.450721 0.055288 0.093750
diff --git a/ai/data/stain/val/labels/kimchi_058.txt b/ai/data/stain/val/labels/kimchi_058.txt
new file mode 100644
index 000000000..807e2b265
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_058.txt
@@ -0,0 +1,3 @@
+4 0.230769 0.532452 0.048077 0.165865
+4 0.346154 0.614183 0.129808 0.156250
+4 0.725962 0.707933 0.091346 0.199519
diff --git a/ai/data/stain/val/labels/kimchi_062.txt b/ai/data/stain/val/labels/kimchi_062.txt
new file mode 100644
index 000000000..ac6a70468
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_062.txt
@@ -0,0 +1,8 @@
+4 0.545673 0.276442 0.052885 0.052885
+4 0.644231 0.324519 0.033654 0.057692
+4 0.581731 0.408654 0.100962 0.100962
+4 0.498798 0.360577 0.060096 0.062500
+4 0.481971 0.490385 0.036058 0.028846
+4 0.572115 0.492788 0.038462 0.048077
+4 0.593750 0.544471 0.038462 0.026442
+4 0.576923 0.354567 0.091346 0.045673
diff --git a/ai/data/stain/val/labels/kimchi_066.txt b/ai/data/stain/val/labels/kimchi_066.txt
new file mode 100644
index 000000000..cad6c4ac2
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_066.txt
@@ -0,0 +1,7 @@
+4 0.483173 0.320913 0.052885 0.060096
+4 0.554087 0.514423 0.151442 0.105769
+4 0.455529 0.647837 0.069712 0.108173
+4 0.355769 0.665865 0.091346 0.072115
+4 0.266827 0.872596 0.129808 0.076923
+4 0.163462 0.795673 0.038462 0.033654
+4 0.320913 0.753606 0.026442 0.016827
diff --git a/ai/data/stain/val/labels/kimchi_072.txt b/ai/data/stain/val/labels/kimchi_072.txt
new file mode 100644
index 000000000..5a3af7497
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_072.txt
@@ -0,0 +1,3 @@
+4 0.457933 0.596154 0.088942 0.110577
+4 0.533654 0.521635 0.019231 0.048077
+4 0.540865 0.468750 0.019231 0.019231
diff --git a/ai/data/stain/val/labels/kimchi_083.txt b/ai/data/stain/val/labels/kimchi_083.txt
new file mode 100644
index 000000000..c3a1995d3
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_083.txt
@@ -0,0 +1,3 @@
+4 0.361779 0.258413 0.045673 0.074519
+4 0.432692 0.358173 0.158654 0.177885
+4 0.429087 0.362981 0.108173 0.057692
diff --git a/ai/data/stain/val/labels/kimchi_089.txt b/ai/data/stain/val/labels/kimchi_089.txt
new file mode 100644
index 000000000..f1cd69be2
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_089.txt
@@ -0,0 +1,2 @@
+4 0.324519 0.591346 0.163462 0.086538
+4 0.353365 0.527644 0.052885 0.060096
diff --git a/ai/data/stain/val/labels/kimchi_090.txt b/ai/data/stain/val/labels/kimchi_090.txt
new file mode 100644
index 000000000..7c14cfce2
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_090.txt
@@ -0,0 +1,4 @@
+4 0.544471 0.472356 0.084135 0.069712
+4 0.697115 0.462740 0.028846 0.026442
+4 0.265625 0.457933 0.036058 0.026442
+4 0.080529 0.468750 0.060096 0.043269
diff --git a/ai/data/stain/val/labels/kimchi_095.txt b/ai/data/stain/val/labels/kimchi_095.txt
new file mode 100644
index 000000000..4e7ce22c1
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_095.txt
@@ -0,0 +1,6 @@
+4 0.147837 0.010817 0.036058 0.016827
+4 0.331731 0.271635 0.052885 0.072115
+4 0.033654 0.176683 0.019231 0.016827
+4 0.485577 0.531250 0.062500 0.091346
+4 0.479567 0.704327 0.031250 0.033654
+4 0.337740 0.826923 0.026442 0.024038
diff --git a/ai/data/stain/val/labels/kimchi_096.txt b/ai/data/stain/val/labels/kimchi_096.txt
new file mode 100644
index 000000000..e5db54238
--- /dev/null
+++ b/ai/data/stain/val/labels/kimchi_096.txt
@@ -0,0 +1,9 @@
+4 0.167067 0.275240 0.093750 0.088942
+4 0.155048 0.406250 0.117788 0.125000
+4 0.137019 0.212740 0.019231 0.016827
+4 0.185096 0.217548 0.024038 0.016827
+4 0.828125 0.323317 0.146635 0.141827
+4 0.754808 0.406250 0.028846 0.033654
+4 0.721154 0.441106 0.033654 0.040865
+4 0.052885 0.330529 0.033654 0.036058
+4 0.096154 0.313702 0.019231 0.031250
diff --git a/ai/data/stain/val/labels/lipstick_10.txt b/ai/data/stain/val/labels/lipstick_10.txt
new file mode 100644
index 000000000..6962099a0
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_10.txt
@@ -0,0 +1 @@
+5 0.640000 0.530055 0.109091 0.207650
diff --git a/ai/data/stain/val/labels/lipstick_16.txt b/ai/data/stain/val/labels/lipstick_16.txt
new file mode 100644
index 000000000..aee6775b1
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_16.txt
@@ -0,0 +1,3 @@
+5 0.449091 0.521858 0.185455 0.191257
+5 0.543636 0.428962 0.040000 0.060109
+5 0.643636 0.527322 0.065455 0.103825
diff --git a/ai/data/stain/val/labels/lipstick_18.txt b/ai/data/stain/val/labels/lipstick_18.txt
new file mode 100644
index 000000000..4a81a8c94
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_18.txt
@@ -0,0 +1 @@
+5 0.589091 0.669399 0.080000 0.060109
diff --git a/ai/data/stain/val/labels/lipstick_22.txt b/ai/data/stain/val/labels/lipstick_22.txt
new file mode 100644
index 000000000..174f6d8e8
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_22.txt
@@ -0,0 +1 @@
+5 0.708333 0.464883 0.071429 0.026756
diff --git a/ai/data/stain/val/labels/lipstick_31.txt b/ai/data/stain/val/labels/lipstick_31.txt
new file mode 100644
index 000000000..6d3aa9480
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_31.txt
@@ -0,0 +1 @@
+5 0.755952 0.576923 0.154762 0.103679
diff --git a/ai/data/stain/val/labels/lipstick_4.txt b/ai/data/stain/val/labels/lipstick_4.txt
new file mode 100644
index 000000000..5729d7851
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_4.txt
@@ -0,0 +1 @@
+5 0.437778 0.717778 0.448889 0.200000
diff --git a/ai/data/stain/val/labels/lipstick_40.txt b/ai/data/stain/val/labels/lipstick_40.txt
new file mode 100644
index 000000000..337c97d37
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_40.txt
@@ -0,0 +1 @@
+5 0.405462 0.554245 0.298319 0.221698
diff --git a/ai/data/stain/val/labels/lipstick_47.txt b/ai/data/stain/val/labels/lipstick_47.txt
new file mode 100644
index 000000000..fe76bc097
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_47.txt
@@ -0,0 +1,3 @@
+5 0.341085 0.520513 0.317829 0.282051
+5 0.288760 0.323077 0.127907 0.153846
+5 0.428295 0.402564 0.151163 0.138462
diff --git a/ai/data/stain/val/labels/lipstick_50.txt b/ai/data/stain/val/labels/lipstick_50.txt
new file mode 100644
index 000000000..c8a23530f
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_50.txt
@@ -0,0 +1,2 @@
+5 0.238235 0.469595 0.088235 0.222973
+5 0.316176 0.760135 0.197059 0.195946
diff --git a/ai/data/stain/val/labels/lipstick_51.txt b/ai/data/stain/val/labels/lipstick_51.txt
new file mode 100644
index 000000000..aad322427
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_51.txt
@@ -0,0 +1,2 @@
+5 0.315217 0.478142 0.094203 0.081967
+5 0.324275 0.442623 0.054348 0.054645
diff --git a/ai/data/stain/val/labels/lipstick_52.txt b/ai/data/stain/val/labels/lipstick_52.txt
new file mode 100644
index 000000000..ed3e7c5e3
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_52.txt
@@ -0,0 +1,3 @@
+5 0.356982 0.340708 0.069820 0.238938
+5 0.476351 0.128319 0.033784 0.115044
+5 0.581081 0.336283 0.166667 0.460177
diff --git a/ai/data/stain/val/labels/lipstick_54.txt b/ai/data/stain/val/labels/lipstick_54.txt
new file mode 100644
index 000000000..9e1f1ab85
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_54.txt
@@ -0,0 +1,7 @@
+5 0.587121 0.321990 0.166667 0.225131
+5 0.700758 0.565445 0.143939 0.104712
+5 0.717803 0.672775 0.178030 0.120419
+5 0.634470 0.764398 0.132576 0.104712
+5 0.609848 0.866492 0.196970 0.151832
+5 0.371212 0.717277 0.181818 0.104712
+5 0.371212 0.801047 0.151515 0.136126
diff --git a/ai/data/stain/val/labels/lipstick_59.txt b/ai/data/stain/val/labels/lipstick_59.txt
new file mode 100644
index 000000000..0566723a7
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_59.txt
@@ -0,0 +1 @@
+5 0.445000 0.508929 0.243333 0.279762
diff --git a/ai/data/stain/val/labels/lipstick_60.txt b/ai/data/stain/val/labels/lipstick_60.txt
new file mode 100644
index 000000000..2fe9fe9a5
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_60.txt
@@ -0,0 +1,2 @@
+5 0.500000 0.590164 0.294545 0.349727
+5 0.552727 0.382514 0.101818 0.142077
diff --git a/ai/data/stain/val/labels/lipstick_66.txt b/ai/data/stain/val/labels/lipstick_66.txt
new file mode 100644
index 000000000..35f9887ff
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_66.txt
@@ -0,0 +1,4 @@
+5 0.715847 0.485455 0.185792 0.098182
+5 0.109290 0.474545 0.065574 0.025455
+5 0.284153 0.820000 0.065574 0.061818
+5 0.836066 0.825455 0.032787 0.072727
diff --git a/ai/data/stain/val/labels/lipstick_71.txt b/ai/data/stain/val/labels/lipstick_71.txt
new file mode 100644
index 000000000..b5067bc9c
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_71.txt
@@ -0,0 +1 @@
+5 0.484496 0.366667 0.093023 0.117949
diff --git a/ai/data/stain/val/labels/lipstick_8.txt b/ai/data/stain/val/labels/lipstick_8.txt
new file mode 100644
index 000000000..a2b43398b
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_8.txt
@@ -0,0 +1,3 @@
+5 0.494932 0.494118 0.077703 0.141176
+5 0.547297 0.635294 0.060811 0.105882
+5 0.596284 0.955882 0.050676 0.088235
diff --git a/ai/data/stain/val/labels/lipstick_85.txt b/ai/data/stain/val/labels/lipstick_85.txt
new file mode 100644
index 000000000..c85872e73
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_85.txt
@@ -0,0 +1 @@
+5 0.530909 0.450820 0.101818 0.136612
diff --git a/ai/data/stain/val/labels/lipstick_89.txt b/ai/data/stain/val/labels/lipstick_89.txt
new file mode 100644
index 000000000..bdf91befa
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_89.txt
@@ -0,0 +1 @@
+5 0.344262 0.263636 0.338798 0.207273
diff --git a/ai/data/stain/val/labels/lipstick_90.txt b/ai/data/stain/val/labels/lipstick_90.txt
new file mode 100644
index 000000000..1e12af151
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_90.txt
@@ -0,0 +1 @@
+5 0.380000 0.508889 0.244444 0.120000
diff --git a/ai/data/stain/val/labels/lipstick_91.txt b/ai/data/stain/val/labels/lipstick_91.txt
new file mode 100644
index 000000000..4bda92c98
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_91.txt
@@ -0,0 +1 @@
+5 0.583333 0.593333 0.357143 0.193333
diff --git a/ai/data/stain/val/labels/lipstick_93.txt b/ai/data/stain/val/labels/lipstick_93.txt
new file mode 100644
index 000000000..2ab767b39
--- /dev/null
+++ b/ai/data/stain/val/labels/lipstick_93.txt
@@ -0,0 +1,12 @@
+5 0.433333 0.571111 0.164444 0.146667
+5 0.562222 0.675556 0.164444 0.142222
+5 0.377778 0.806667 0.097778 0.102222
+5 0.571111 0.902222 0.111111 0.097778
+5 0.742222 0.366667 0.133333 0.111111
+5 0.960000 0.462222 0.071111 0.115556
+5 0.953333 0.717778 0.075556 0.137778
+5 0.755556 0.691111 0.062222 0.093333
+5 0.048889 0.802222 0.071111 0.093333
+5 0.051111 0.473333 0.057778 0.057778
+5 0.766667 0.228889 0.084444 0.057778
+5 0.713333 0.315556 0.128889 0.080000
diff --git a/ai/data/stain/val/labels/mustard_003.txt b/ai/data/stain/val/labels/mustard_003.txt
new file mode 100644
index 000000000..d5ccb9ed5
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_003.txt
@@ -0,0 +1,9 @@
+6 0.587329 0.248555 0.058219 0.046243
+6 0.642123 0.745665 0.133562 0.358382
+6 0.496575 0.907514 0.068493 0.127168
+4 0.270548 0.196532 0.041096 0.080925
+4 0.311644 0.306358 0.034247 0.080925
+4 0.304795 0.433526 0.041096 0.057803
+4 0.239726 0.560694 0.054795 0.104046
+4 0.210616 0.696532 0.044521 0.179191
+6 0.753425 0.696532 0.054795 0.121387
diff --git a/ai/data/stain/val/labels/mustard_018.txt b/ai/data/stain/val/labels/mustard_018.txt
new file mode 100644
index 000000000..fb5a114b9
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_018.txt
@@ -0,0 +1 @@
+6 0.485119 0.503333 0.244048 0.266667
diff --git a/ai/data/stain/val/labels/mustard_024.txt b/ai/data/stain/val/labels/mustard_024.txt
new file mode 100644
index 000000000..9d94be63c
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_024.txt
@@ -0,0 +1,6 @@
+6 0.289091 0.390710 0.076364 0.213115
+6 0.209091 0.568306 0.032727 0.054645
+6 0.312727 0.540984 0.036364 0.054645
+6 0.341818 0.633880 0.050909 0.109290
+6 0.363636 0.795082 0.036364 0.038251
+6 0.380000 0.860656 0.025455 0.038251
diff --git a/ai/data/stain/val/labels/mustard_027.txt b/ai/data/stain/val/labels/mustard_027.txt
new file mode 100644
index 000000000..709d3e604
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_027.txt
@@ -0,0 +1,2 @@
+6 0.511905 0.353333 0.130952 0.060000
+6 0.452381 0.786667 0.047619 0.033333
diff --git a/ai/data/stain/val/labels/mustard_045.txt b/ai/data/stain/val/labels/mustard_045.txt
new file mode 100644
index 000000000..9939d1c9e
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_045.txt
@@ -0,0 +1 @@
+6 0.402222 0.893333 0.146667 0.186667
diff --git a/ai/data/stain/val/labels/mustard_050.txt b/ai/data/stain/val/labels/mustard_050.txt
new file mode 100644
index 000000000..b8bcda5a2
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_050.txt
@@ -0,0 +1,4 @@
+6 0.401818 0.513661 0.323636 0.207650
+6 0.730909 0.450820 0.174545 0.234973
+6 0.641818 0.713115 0.149091 0.224044
+6 0.349091 0.606557 0.065455 0.131148
diff --git a/ai/data/stain/val/labels/mustard_051.txt b/ai/data/stain/val/labels/mustard_051.txt
new file mode 100644
index 000000000..8fe603c13
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_051.txt
@@ -0,0 +1 @@
+6 0.672131 0.496364 0.065574 0.069091
diff --git a/ai/data/stain/val/labels/mustard_053.txt b/ai/data/stain/val/labels/mustard_053.txt
new file mode 100644
index 000000000..de23ed534
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_053.txt
@@ -0,0 +1,2 @@
+6 0.637066 0.399485 0.115830 0.128866
+6 0.704633 0.768041 0.057915 0.103093
diff --git a/ai/data/stain/val/labels/mustard_054.txt b/ai/data/stain/val/labels/mustard_054.txt
new file mode 100644
index 000000000..b84dcdb14
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_054.txt
@@ -0,0 +1,6 @@
+6 0.717778 0.271111 0.253333 0.204444
+6 0.495556 0.586667 0.084444 0.533333
+6 0.862222 0.813333 0.151111 0.364444
+6 0.608889 0.542222 0.782222 0.906667
+6 0.673333 0.557778 0.120000 0.066667
+6 0.835556 0.522222 0.106667 0.200000
diff --git a/ai/data/stain/val/labels/mustard_065.txt b/ai/data/stain/val/labels/mustard_065.txt
new file mode 100644
index 000000000..cec5754d9
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_065.txt
@@ -0,0 +1,4 @@
+6 0.412568 0.641818 0.081967 0.054545
+6 0.344262 0.776364 0.153005 0.178182
+6 0.486339 0.769091 0.054645 0.047273
+6 0.510929 0.670909 0.038251 0.047273
diff --git a/ai/data/stain/val/labels/mustard_066.txt b/ai/data/stain/val/labels/mustard_066.txt
new file mode 100644
index 000000000..ce31e123a
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_066.txt
@@ -0,0 +1 @@
+6 0.511194 0.454787 0.067164 0.101064
diff --git a/ai/data/stain/val/labels/mustard_074.txt b/ai/data/stain/val/labels/mustard_074.txt
new file mode 100644
index 000000000..59434df9f
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_074.txt
@@ -0,0 +1,6 @@
+6 0.218182 0.415301 0.225455 0.426230
+6 0.327273 0.603825 0.269091 0.355191
+6 0.541818 0.721311 0.298182 0.284153
+6 0.761818 0.650273 0.229091 0.207650
+6 0.240000 0.163934 0.123636 0.142077
+6 0.485455 0.513661 0.767273 0.972678
diff --git a/ai/data/stain/val/labels/mustard_083.txt b/ai/data/stain/val/labels/mustard_083.txt
new file mode 100644
index 000000000..9ceace350
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_083.txt
@@ -0,0 +1,9 @@
+6 0.262548 0.172680 0.092664 0.128866
+6 0.370656 0.265464 0.115830 0.149485
+6 0.428571 0.376289 0.092664 0.103093
+6 0.484556 0.471649 0.042471 0.046392
+6 0.743243 0.579897 0.050193 0.087629
+6 0.656371 0.659794 0.108108 0.144330
+6 0.727799 0.690722 0.027027 0.041237
+6 0.799228 0.796392 0.069498 0.128866
+6 0.727799 0.804124 0.104247 0.144330
diff --git a/ai/data/stain/val/labels/mustard_084.txt b/ai/data/stain/val/labels/mustard_084.txt
new file mode 100644
index 000000000..a0766336c
--- /dev/null
+++ b/ai/data/stain/val/labels/mustard_084.txt
@@ -0,0 +1,4 @@
+6 0.196429 0.590000 0.095238 0.060000
+6 0.455357 0.643333 0.089286 0.046667
+6 0.735119 0.721667 0.065476 0.063333
+6 0.666667 0.255000 0.071429 0.050000
diff --git a/ai/data/stain/val/labels/oil_001.txt b/ai/data/stain/val/labels/oil_001.txt
new file mode 100644
index 000000000..885916e72
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_001.txt
@@ -0,0 +1 @@
+7 0.750000 0.729167 0.393333 0.529762
diff --git a/ai/data/stain/val/labels/oil_003.txt b/ai/data/stain/val/labels/oil_003.txt
new file mode 100644
index 000000000..bccc0133b
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_003.txt
@@ -0,0 +1 @@
+7 0.484556 0.440722 0.552124 0.314433
diff --git a/ai/data/stain/val/labels/oil_013.txt b/ai/data/stain/val/labels/oil_013.txt
new file mode 100644
index 000000000..446d33e86
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_013.txt
@@ -0,0 +1 @@
+7 0.590909 0.560109 0.156364 0.191257
diff --git a/ai/data/stain/val/labels/oil_015.txt b/ai/data/stain/val/labels/oil_015.txt
new file mode 100644
index 000000000..b7e4e2201
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_015.txt
@@ -0,0 +1,3 @@
+7 0.251366 0.738182 0.262295 0.240000
+7 0.584699 0.734545 0.371585 0.472727
+7 0.311475 0.436364 0.491803 0.174545
diff --git a/ai/data/stain/val/labels/oil_018.txt b/ai/data/stain/val/labels/oil_018.txt
new file mode 100644
index 000000000..b527c79c0
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_018.txt
@@ -0,0 +1,2 @@
+7 0.486622 0.866071 0.137124 0.220238
+7 0.687291 0.773810 0.090301 0.142857
diff --git a/ai/data/stain/val/labels/oil_022.txt b/ai/data/stain/val/labels/oil_022.txt
new file mode 100644
index 000000000..ec59efeae
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_022.txt
@@ -0,0 +1,2 @@
+7 0.557778 0.395556 0.164444 0.088889
+7 0.500000 0.753333 0.288889 0.315556
diff --git a/ai/data/stain/val/labels/oil_028.txt b/ai/data/stain/val/labels/oil_028.txt
new file mode 100644
index 000000000..adffd85ea
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_028.txt
@@ -0,0 +1,4 @@
+7 0.443452 0.528333 0.125000 0.096667
+7 0.392857 0.638333 0.130952 0.076667
+7 0.747024 0.306667 0.089286 0.073333
+7 0.729167 0.383333 0.113095 0.073333
diff --git a/ai/data/stain/val/labels/oil_034.txt b/ai/data/stain/val/labels/oil_034.txt
new file mode 100644
index 000000000..5f762d333
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_034.txt
@@ -0,0 +1 @@
+7 0.551237 0.199438 0.176678 0.252809
diff --git a/ai/data/stain/val/labels/oil_037.txt b/ai/data/stain/val/labels/oil_037.txt
new file mode 100644
index 000000000..4234ea66e
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_037.txt
@@ -0,0 +1,3 @@
+7 0.455598 0.358247 0.123552 0.118557
+7 0.364865 0.582474 0.073359 0.092784
+7 0.563707 0.636598 0.054054 0.067010
diff --git a/ai/data/stain/val/labels/oil_077.txt b/ai/data/stain/val/labels/oil_077.txt
new file mode 100644
index 000000000..59d063736
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_077.txt
@@ -0,0 +1 @@
+7 0.198454 0.845560 0.345361 0.247104
diff --git a/ai/data/stain/val/labels/oil_078.txt b/ai/data/stain/val/labels/oil_078.txt
new file mode 100644
index 000000000..a2eaba60e
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_078.txt
@@ -0,0 +1,3 @@
+7 0.396907 0.416988 0.226804 0.200772
+7 0.533505 0.297297 0.190722 0.162162
+7 0.515464 0.637066 0.103093 0.123552
diff --git a/ai/data/stain/val/labels/oil_079.txt b/ai/data/stain/val/labels/oil_079.txt
new file mode 100644
index 000000000..49f583af4
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_079.txt
@@ -0,0 +1 @@
+7 0.438144 0.449807 0.185567 0.150579
diff --git a/ai/data/stain/val/labels/oil_081.txt b/ai/data/stain/val/labels/oil_081.txt
new file mode 100644
index 000000000..50e5677c0
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_081.txt
@@ -0,0 +1 @@
+7 0.492268 0.583012 0.190722 0.247104
diff --git a/ai/data/stain/val/labels/oil_096.txt b/ai/data/stain/val/labels/oil_096.txt
new file mode 100644
index 000000000..b50e47224
--- /dev/null
+++ b/ai/data/stain/val/labels/oil_096.txt
@@ -0,0 +1,4 @@
+7 0.800000 0.342014 0.171429 0.100694
+7 0.711429 0.612847 0.337143 0.184028
+7 0.422857 0.590278 0.148571 0.090278
+7 0.705714 0.835069 0.108571 0.079861
diff --git a/ai/data/stain/val/labels/wine_006.txt b/ai/data/stain/val/labels/wine_006.txt
new file mode 100644
index 000000000..76d992d8b
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_006.txt
@@ -0,0 +1 @@
+8 0.574545 0.295082 0.312727 0.229508
diff --git a/ai/data/stain/val/labels/wine_007.txt b/ai/data/stain/val/labels/wine_007.txt
new file mode 100644
index 000000000..8a1a839a9
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_007.txt
@@ -0,0 +1,2 @@
+8 0.254545 0.472678 0.145455 0.202186
+8 0.321818 0.849727 0.200000 0.300546
diff --git a/ai/data/stain/val/labels/wine_023.txt b/ai/data/stain/val/labels/wine_023.txt
new file mode 100644
index 000000000..2bc6918e3
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_023.txt
@@ -0,0 +1 @@
+8 0.403010 0.723214 0.143813 0.136905
diff --git a/ai/data/stain/val/labels/wine_025.txt b/ai/data/stain/val/labels/wine_025.txt
new file mode 100644
index 000000000..f684bc140
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_025.txt
@@ -0,0 +1,3 @@
+8 0.675000 0.571429 0.043333 0.107143
+8 0.655000 0.699405 0.043333 0.089286
+8 0.646667 0.806548 0.053333 0.077381
diff --git a/ai/data/stain/val/labels/wine_026.txt b/ai/data/stain/val/labels/wine_026.txt
new file mode 100644
index 000000000..eae92870c
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_026.txt
@@ -0,0 +1,4 @@
+8 0.609434 0.418421 0.215094 0.363158
+8 0.554717 0.678947 0.233962 0.242105
+8 0.722642 0.563158 0.079245 0.442105
+8 0.866038 0.423684 0.109434 0.152632
diff --git a/ai/data/stain/val/labels/wine_027.txt b/ai/data/stain/val/labels/wine_027.txt
new file mode 100644
index 000000000..dc9e7ace0
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_027.txt
@@ -0,0 +1,3 @@
+8 0.441818 0.480874 0.265455 0.404372
+8 0.545455 0.448087 0.036364 0.087432
+8 0.652727 0.532787 0.149091 0.114754
diff --git a/ai/data/stain/val/labels/wine_032.txt b/ai/data/stain/val/labels/wine_032.txt
new file mode 100644
index 000000000..154025b81
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_032.txt
@@ -0,0 +1 @@
+8 0.520000 0.644809 0.290909 0.163934
diff --git a/ai/data/stain/val/labels/wine_044.txt b/ai/data/stain/val/labels/wine_044.txt
new file mode 100644
index 000000000..a73db417b
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_044.txt
@@ -0,0 +1,2 @@
+8 0.451737 0.221649 0.084942 0.123711
+8 0.774131 0.293814 0.081081 0.103093
diff --git a/ai/data/stain/val/labels/wine_049.txt b/ai/data/stain/val/labels/wine_049.txt
new file mode 100644
index 000000000..d2c64ac18
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_049.txt
@@ -0,0 +1 @@
+8 0.345455 0.756831 0.276364 0.355191
diff --git a/ai/data/stain/val/labels/wine_051.txt b/ai/data/stain/val/labels/wine_051.txt
new file mode 100644
index 000000000..1fd776a0d
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_051.txt
@@ -0,0 +1 @@
+8 0.600583 0.302721 0.064140 0.346939
diff --git a/ai/data/stain/val/labels/wine_052.txt b/ai/data/stain/val/labels/wine_052.txt
new file mode 100644
index 000000000..4ea4e23e4
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_052.txt
@@ -0,0 +1,2 @@
+8 0.363806 0.321809 0.108209 0.196809
+8 0.363806 0.585106 0.070896 0.095745
diff --git a/ai/data/stain/val/labels/wine_053.txt b/ai/data/stain/val/labels/wine_053.txt
new file mode 100644
index 000000000..86eae1c7b
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_053.txt
@@ -0,0 +1,6 @@
+8 0.218750 0.394578 0.101974 0.162651
+8 0.273026 0.563253 0.092105 0.271084
+8 0.304276 0.756024 0.108553 0.150602
+8 0.536184 0.864458 0.256579 0.090361
+8 0.720395 0.650602 0.065789 0.349398
+8 0.366776 0.301205 0.121711 0.084337
diff --git a/ai/data/stain/val/labels/wine_055.txt b/ai/data/stain/val/labels/wine_055.txt
new file mode 100644
index 000000000..fb96b8b94
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_055.txt
@@ -0,0 +1 @@
+8 0.312709 0.291667 0.056856 0.095238
diff --git a/ai/data/stain/val/labels/wine_057.txt b/ai/data/stain/val/labels/wine_057.txt
new file mode 100644
index 000000000..1a0ed1e41
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_057.txt
@@ -0,0 +1,3 @@
+8 0.676364 0.366120 0.065455 0.142077
+8 0.605455 0.606557 0.178182 0.240437
+8 0.672727 0.775956 0.065455 0.109290
diff --git a/ai/data/stain/val/labels/wine_062.txt b/ai/data/stain/val/labels/wine_062.txt
new file mode 100644
index 000000000..37055c32a
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_062.txt
@@ -0,0 +1,5 @@
+8 0.696335 0.676136 0.188482 0.094697
+8 0.481675 0.821970 0.115183 0.219697
+8 0.549738 0.818182 0.115183 0.181818
+8 0.722513 0.840909 0.157068 0.196970
+8 0.387435 0.566288 0.052356 0.026515
diff --git a/ai/data/stain/val/labels/wine_068.txt b/ai/data/stain/val/labels/wine_068.txt
new file mode 100644
index 000000000..0dd3df904
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_068.txt
@@ -0,0 +1,2 @@
+8 0.592727 0.644809 0.196364 0.557377
+8 0.541818 0.775956 0.254545 0.295082
diff --git a/ai/data/stain/val/labels/wine_072.txt b/ai/data/stain/val/labels/wine_072.txt
new file mode 100644
index 000000000..02d859eb2
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_072.txt
@@ -0,0 +1 @@
+8 0.280822 0.427746 0.171233 0.751445
diff --git a/ai/data/stain/val/labels/wine_074.txt b/ai/data/stain/val/labels/wine_074.txt
new file mode 100644
index 000000000..e2f88fdff
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_074.txt
@@ -0,0 +1,5 @@
+8 0.488462 0.074742 0.046154 0.056701
+8 0.628846 0.188144 0.280769 0.077320
+8 0.409615 0.577320 0.119231 0.154639
+8 0.776923 0.505155 0.215385 0.134021
+8 0.786538 0.706186 0.165385 0.226804
diff --git a/ai/data/stain/val/labels/wine_077.txt b/ai/data/stain/val/labels/wine_077.txt
new file mode 100644
index 000000000..970d01962
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_077.txt
@@ -0,0 +1,3 @@
+8 0.603636 0.483607 0.414545 0.256831
+8 0.267273 0.639344 0.083636 0.306011
+8 0.523636 0.625683 0.116364 0.169399
diff --git a/ai/data/stain/val/labels/wine_082.txt b/ai/data/stain/val/labels/wine_082.txt
new file mode 100644
index 000000000..f310ae200
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_082.txt
@@ -0,0 +1,4 @@
+8 0.605000 0.422619 0.490000 0.452381
+8 0.573333 0.729167 0.320000 0.541667
+8 0.835000 0.610119 0.330000 0.446429
+8 0.693333 0.151786 0.286667 0.184524
diff --git a/ai/data/stain/val/labels/wine_083.txt b/ai/data/stain/val/labels/wine_083.txt
new file mode 100644
index 000000000..b722c4715
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_083.txt
@@ -0,0 +1,7 @@
+8 0.685567 0.463320 0.154639 0.115830
+8 0.481959 0.482625 0.108247 0.084942
+8 0.206186 0.671815 0.103093 0.061776
+8 0.608247 0.146718 0.082474 0.061776
+8 0.551546 0.723938 0.185567 0.081081
+8 0.597938 0.650579 0.041237 0.281853
+8 0.548969 0.847490 0.067010 0.042471
diff --git a/ai/data/stain/val/labels/wine_086.txt b/ai/data/stain/val/labels/wine_086.txt
new file mode 100644
index 000000000..ccfe4bbe5
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_086.txt
@@ -0,0 +1 @@
+8 0.472973 0.427835 0.104247 0.082474
diff --git a/ai/data/stain/val/labels/wine_091.txt b/ai/data/stain/val/labels/wine_091.txt
new file mode 100644
index 000000000..299d30797
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_091.txt
@@ -0,0 +1,5 @@
+8 0.251667 0.407738 0.256667 0.327381
+8 0.725000 0.330357 0.543333 0.184524
+8 0.588333 0.580357 0.223333 0.482143
+8 0.241667 0.735119 0.410000 0.529762
+8 0.740000 0.758929 0.306667 0.482143
diff --git a/ai/data/stain/val/labels/wine_093.txt b/ai/data/stain/val/labels/wine_093.txt
new file mode 100644
index 000000000..fdc084f97
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_093.txt
@@ -0,0 +1,4 @@
+8 0.293814 0.279923 0.195876 0.119691
+8 0.451031 0.386100 0.242268 0.138996
+8 0.659794 0.494208 0.257732 0.123552
+8 0.824742 0.571429 0.226804 0.131274
diff --git a/ai/data/stain/val/labels/wine_095.txt b/ai/data/stain/val/labels/wine_095.txt
new file mode 100644
index 000000000..7b9a6330c
--- /dev/null
+++ b/ai/data/stain/val/labels/wine_095.txt
@@ -0,0 +1 @@
+8 0.536364 0.543716 0.214545 0.311475
diff --git a/ai/data/stain/val/labels/wine_099.txt b/ai/data/stain/val/labels/wine_099.txt
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index 000000000..54e94e302
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+++ b/ai/data/stain/val/labels/wine_099.txt
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+8 0.427336 0.540230 0.169550 0.436782
diff --git a/ai/data/symbol/data.yaml b/ai/data/symbol/data.yaml
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index 000000000..7441e9f1f
--- /dev/null
+++ b/ai/data/symbol/data.yaml
@@ -0,0 +1,49 @@
+train:  /home/t25119/v109.src/ai/data/symbol/tain/images
+val:  /home/t25119/v109.src/ai/data/symbol/val/images
+test:  /home/t25119/v109.src/ai/data/symbol/test/images
+
+nc: 43
+names:
+  - 30C                         # 0
+  - 40C                         # 1
+  - 50C                         # 2
+  - 60C                         # 3
+  - 70C                         # 4
+  - 95C                         # 5
+  - DN_bleach                   # 6
+  - DN_dry                      # 7
+  - DN_dry_clean                # 8
+  - DN_iron                     # 9
+  - DN_steam                    # 10
+  - DN_tumble_dry               # 11
+  - DN_wash                     # 12
+  - DN_wet_clean                # 13
+  - DN_wring                    # 14
+  - bleach                      # 15
+  - chlorine_bleach             # 16
+  - non_chlorine_bleach         # 17
+  - drip_dry                    # 18
+  - drip_dry_in_shade           # 19
+  - dry_flat                    # 20
+  - dry_flat_in_shade           # 21
+  - line_dry                    # 22
+  - line_dry_in_shade           # 23
+  - natural_dry                 # 24
+  - shade_dry                   # 25
+  - dry_clean                   # 26   
+  - dry_clean_any_solvent_except_trichloroethylene   # 27
+  - dry_clean_petrol_only       # 28
+  - hand_wash                   # 29
+  - machine_wash                # 30
+  - iron                        # 31
+  - iron_high                   # 32
+  - iron_medium                 # 33
+  - iron_low                    # 34
+  - steam                       # 35
+  - tumble_dry_normal           # 36
+  - tumble_dry_low              # 37
+  - tumble_dry_medium           # 38
+  - tumble_dry_high             # 39
+  - tumble_dry_no_heat          # 40
+  - wet_clean                   # 41
+  - wring                       # 42
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+12 0.267692 0.802331 0.145172 0.099206
+6 0.420800 0.803075 0.152447 0.099206
+33 0.589451 0.807912 0.150463 0.094494
+26 0.751488 0.808036 0.139881 0.099206
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+0 0.170635 0.702753 0.130291 0.066220
+6 0.305556 0.700769 0.146164 0.076637
+37 0.434689 0.699157 0.114087 0.083333
+33 0.562169 0.703125 0.140873 0.071925
+8 0.708995 0.703869 0.156746 0.078373
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--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_02.txt
@@ -0,0 +1,6 @@
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+42 0.535549 0.630332 0.286045 0.108879
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_03.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_03.txt
new file mode 100644
index 000000000..365c14541
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_03.txt
@@ -0,0 +1,5 @@
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+9 0.576554 0.825149 0.116071 0.088790
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_04.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_04.txt
new file mode 100644
index 000000000..39751c213
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_04.txt
@@ -0,0 +1,15 @@
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+6 0.346065 0.754712 0.112103 0.075893
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_05.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_05.txt
new file mode 100644
index 000000000..3709229b2
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_05.txt
@@ -0,0 +1,3 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_06.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_06.txt
new file mode 100644
index 000000000..6a6219d4d
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_06.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_07.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_07.txt
new file mode 100644
index 000000000..4a0a4f8ff
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_07.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_08.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_08.txt
new file mode 100644
index 000000000..db27e4713
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_08.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_09.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_09.txt
new file mode 100644
index 000000000..eb14e456e
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_09.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_10.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_10.txt
new file mode 100644
index 000000000..0b3ce2eb6
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_10.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_11.txt b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_11.txt
new file mode 100644
index 000000000..108ccf3ae
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250514_212707873_11.txt
@@ -0,0 +1,12 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393.txt
new file mode 100644
index 000000000..88b7f7505
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393.txt
@@ -0,0 +1,6 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_01.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_01.txt
new file mode 100644
index 000000000..e6b5ccbe4
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_01.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_02.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_02.txt
new file mode 100644
index 000000000..21d751fa7
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_02.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_04.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_04.txt
new file mode 100644
index 000000000..8dca967b0
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_04.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_05.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_05.txt
new file mode 100644
index 000000000..47dacfe41
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_05.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_06.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_06.txt
new file mode 100644
index 000000000..ceb98e1d6
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_06.txt
@@ -0,0 +1,6 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_07.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_07.txt
new file mode 100644
index 000000000..438bb6cc2
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_07.txt
@@ -0,0 +1,6 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_08.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_08.txt
new file mode 100644
index 000000000..7c2b99a2c
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_08.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_09.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_09.txt
new file mode 100644
index 000000000..96530b2bb
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_09.txt
@@ -0,0 +1,6 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_10.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_10.txt
new file mode 100644
index 000000000..97704f38b
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_111821393_10.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372.txt
new file mode 100644
index 000000000..38ff1b5ce
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_01.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_01.txt
new file mode 100644
index 000000000..2257710f8
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_01.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_02.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_02.txt
new file mode 100644
index 000000000..cd4e7544f
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_02.txt
@@ -0,0 +1,5 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_03.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_03.txt
new file mode 100644
index 000000000..ee1ad13a5
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_03.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_04.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_04.txt
new file mode 100644
index 000000000..aa8dc4f10
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_04.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_05.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_05.txt
new file mode 100644
index 000000000..8df95b285
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_05.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_06.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_06.txt
new file mode 100644
index 000000000..ba8585882
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_06.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_07.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_07.txt
new file mode 100644
index 000000000..b9e983ddb
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_07.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_08.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_08.txt
new file mode 100644
index 000000000..a265206e9
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_08.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_09.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_09.txt
new file mode 100644
index 000000000..6f997ed49
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_09.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_10.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_10.txt
new file mode 100644
index 000000000..11326d76a
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_144454372_10.txt
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765.txt
new file mode 100644
index 000000000..95d8f6149
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765.txt
@@ -0,0 +1,5 @@
+12 0.280556 0.606597 0.092593 0.068750
+14 0.390278 0.623264 0.119444 0.056250
+25 0.494444 0.628472 0.088889 0.068056
+34 0.592593 0.632986 0.096296 0.072917
+26 0.683333 0.639583 0.085185 0.056944
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_01.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_01.txt
new file mode 100644
index 000000000..2406cfb4d
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_01.txt
@@ -0,0 +1,11 @@
+29 0.229880 0.129861 0.049029 0.040278
+6 0.293247 0.130556 0.051804 0.038889
+34 0.357539 0.131250 0.045328 0.040278
+8 0.421369 0.130903 0.050879 0.040972
+11 0.485199 0.130208 0.054579 0.040972
+21 0.549491 0.128472 0.048104 0.033333
+29 0.250694 0.181597 0.053654 0.038194
+6 0.325624 0.182986 0.055504 0.040972
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+20 0.477798 0.181944 0.056429 0.040278
+8 0.550416 0.179167 0.057354 0.043056
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_02.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_02.txt
new file mode 100644
index 000000000..97effdef4
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_02.txt
@@ -0,0 +1,4 @@
+30 0.362963 0.297569 0.096296 0.065972
+9 0.478704 0.286806 0.098148 0.063889
+6 0.598611 0.281597 0.099074 0.064583
+8 0.719907 0.279514 0.100926 0.070139
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_03.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_03.txt
new file mode 100644
index 000000000..4aec88ab3
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_03.txt
@@ -0,0 +1,4 @@
+26 0.421296 0.479167 0.059259 0.051389
+11 0.498148 0.477083 0.059259 0.047222
+34 0.577315 0.480903 0.073148 0.047917
+6 0.650926 0.478472 0.061111 0.045833
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_04.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_04.txt
new file mode 100644
index 000000000..71ef054af
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_04.txt
@@ -0,0 +1,5 @@
+30 0.259722 0.781597 0.112037 0.065972
+6 0.376852 0.788542 0.116667 0.067361
+31 0.490278 0.787500 0.104630 0.069444
+28 0.601389 0.784028 0.106481 0.081944
+18 0.715741 0.794444 0.098148 0.094444
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_05.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_05.txt
new file mode 100644
index 000000000..a3c9e7c4c
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_05.txt
@@ -0,0 +1,7 @@
+29 0.248148 0.379861 0.133333 0.095833
+6 0.378704 0.371181 0.127778 0.093750
+11 0.510185 0.362847 0.120370 0.097917
+21 0.643056 0.363542 0.132407 0.085417
+9 0.330093 0.465278 0.150926 0.095833
+8 0.468056 0.457639 0.119444 0.098611
+41 0.590741 0.456597 0.122222 0.096528
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_06.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_06.txt
new file mode 100644
index 000000000..d66e7cb40
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_06.txt
@@ -0,0 +1,4 @@
+29 0.143386 0.703472 0.085106 0.063889
+26 0.226642 0.705903 0.072155 0.053472
+6 0.298335 0.707639 0.071230 0.047222
+25 0.379741 0.707986 0.076781 0.057639
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_07.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_07.txt
new file mode 100644
index 000000000..6f9259310
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_07.txt
@@ -0,0 +1,5 @@
+12 0.336574 0.711806 0.084259 0.065278
+6 0.442593 0.710417 0.090741 0.069444
+34 0.545370 0.700347 0.094444 0.057639
+28 0.643056 0.688542 0.076852 0.057639
+11 0.741667 0.681597 0.094444 0.078472
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_08.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_08.txt
new file mode 100644
index 000000000..3a92721e9
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_08.txt
@@ -0,0 +1,4 @@
+29 0.376389 0.550694 0.106481 0.066667
+17 0.488889 0.569444 0.112963 0.073611
+33 0.610185 0.574653 0.112963 0.075694
+23 0.732870 0.588194 0.119444 0.076389
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_09.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_09.txt
new file mode 100644
index 000000000..e9c678590
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_09.txt
@@ -0,0 +1,5 @@
+29 0.393981 0.292014 0.091667 0.072917
+6 0.529167 0.296528 0.113889 0.068056
+20 0.667593 0.294097 0.096296 0.067361
+9 0.449074 0.365972 0.107407 0.063889
+8 0.585648 0.361806 0.106481 0.063889
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_10.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_10.txt
new file mode 100644
index 000000000..b76cf393a
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_221400765_10.txt
@@ -0,0 +1,4 @@
+29 0.302778 0.757986 0.094444 0.078472
+6 0.426389 0.753125 0.100926 0.079861
+33 0.544444 0.744097 0.087037 0.070139
+11 0.655093 0.747569 0.097222 0.077083
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225.txt
new file mode 100644
index 000000000..a7e6b4a98
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225.txt
@@ -0,0 +1,9 @@
+6 0.375000 0.479911 0.062831 0.044643
+36 0.445437 0.482639 0.082011 0.060020
+34 0.529101 0.490699 0.093915 0.064732
+8 0.614914 0.492684 0.077712 0.060764
+0 0.377646 0.090774 0.044974 0.047123
+6 0.445933 0.079489 0.061839 0.037450
+37 0.516204 0.079985 0.055556 0.038442
+34 0.588294 0.097098 0.058862 0.045387
+8 0.662533 0.106399 0.061839 0.041667
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_01.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_01.txt
new file mode 100644
index 000000000..f3239b437
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_01.txt
@@ -0,0 +1,6 @@
+1 0.348876 0.535094 0.057540 0.051339
+6 0.415013 0.536334 0.064153 0.045387
+33 0.485450 0.539931 0.066138 0.046627
+8 0.557374 0.531498 0.084325 0.055060
+23 0.631448 0.526290 0.072421 0.049603
+11 0.350860 0.585565 0.066138 0.049603
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_02.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_02.txt
new file mode 100644
index 000000000..f2b1d1f12
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_02.txt
@@ -0,0 +1,5 @@
+29 0.184400 0.693783 0.056796 0.044974
+6 0.184276 0.636243 0.039187 0.050926
+34 0.181176 0.581845 0.040923 0.055225
+21 0.185144 0.523148 0.042411 0.050926
+11 0.187128 0.468750 0.043403 0.051257
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_03.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_03.txt
new file mode 100644
index 000000000..e051c5b2e
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_03.txt
@@ -0,0 +1,15 @@
+1 0.341435 0.370288 0.059854 0.051091
+6 0.413856 0.372024 0.072421 0.043155
+11 0.487599 0.369916 0.070437 0.050347
+34 0.562169 0.371900 0.074735 0.044891
+27 0.641700 0.371156 0.067130 0.044891
+11 0.642196 0.436012 0.074735 0.049603
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+34 0.559524 0.497520 0.067460 0.051091
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+6 0.409722 0.496776 0.074735 0.048115
+1 0.334491 0.433160 0.054563 0.050347
+1 0.337136 0.493180 0.061839 0.055308
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_04.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_04.txt
new file mode 100644
index 000000000..e1fd45b49
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_04.txt
@@ -0,0 +1,6 @@
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+17 0.548776 0.513517 0.067130 0.043403
+12 0.545635 0.550719 0.067460 0.040923
+12 0.658234 0.554688 0.061839 0.043899
+33 0.657242 0.515129 0.070437 0.041667
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_05.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_05.txt
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index 000000000..260f7c86c
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_05.txt
@@ -0,0 +1,6 @@
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+11 0.372354 0.484375 0.106481 0.069444
+17 0.447917 0.419519 0.089616 0.053819
+33 0.524306 0.421503 0.084325 0.051339
+8 0.597388 0.417535 0.077050 0.059276
+23 0.667824 0.415551 0.079034 0.061756
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_06.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_06.txt
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index 000000000..c101bbae9
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_06.txt
@@ -0,0 +1,5 @@
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+14 0.365410 0.728051 0.064815 0.031994
+6 0.431548 0.723090 0.050265 0.033482
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_07.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_07.txt
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index 000000000..6aebd9699
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_07.txt
@@ -0,0 +1,6 @@
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diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_09.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_09.txt
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index 000000000..a8fa4f0e6
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_09.txt
@@ -0,0 +1,5 @@
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+17 0.374504 0.771205 0.091601 0.060764
+33 0.474041 0.765625 0.103505 0.071925
+42 0.573413 0.759177 0.111772 0.041667
+23 0.674107 0.755952 0.089616 0.059028
diff --git a/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_10.txt b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_10.txt
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index 000000000..d0fb2b720
--- /dev/null
+++ b/ai/data/symbol/test/labels/KakaoTalk_20250516_223034225_10.txt
@@ -0,0 +1,4 @@
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index 000000000..5d2b6f752
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+++ b/ai/data/symbol/val/labels/symbol_00003.txt
@@ -0,0 +1,5 @@
+11 0.548958 0.435417 0.047917 0.037500
+8 0.613542 0.409375 0.039583 0.039583
+9 0.671875 0.427083 0.068750 0.054167
+12 0.656250 0.477083 0.066667 0.054167
+6 0.598958 0.455208 0.052083 0.043750
diff --git a/ai/data/symbol/val/labels/symbol_00007.txt b/ai/data/symbol/val/labels/symbol_00007.txt
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index 000000000..69437509d
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00007.txt
@@ -0,0 +1,6 @@
+1 0.646875 0.284375 0.097917 0.093750
+17 0.547917 0.310417 0.070833 0.104167
+9 0.530208 0.221875 0.097917 0.089583
+21 0.417708 0.248958 0.093750 0.085417
+11 0.439583 0.332292 0.091667 0.093750
+42 0.331250 0.348958 0.100000 0.072917
diff --git a/ai/data/symbol/val/labels/symbol_00010.txt b/ai/data/symbol/val/labels/symbol_00010.txt
new file mode 100644
index 000000000..98c8596b2
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00010.txt
@@ -0,0 +1,5 @@
+9 0.384375 0.314583 0.110417 0.100000
+17 0.451042 0.410417 0.072917 0.100000
+23 0.500000 0.496875 0.066667 0.093750
+9 0.563542 0.578125 0.089583 0.089583
+28 0.631250 0.669792 0.095833 0.093750
diff --git a/ai/data/symbol/val/labels/symbol_00011.txt b/ai/data/symbol/val/labels/symbol_00011.txt
new file mode 100644
index 000000000..87d94161f
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00011.txt
@@ -0,0 +1,4 @@
+12 0.512500 0.310417 0.116667 0.100000
+6 0.591667 0.375000 0.095833 0.079167
+34 0.673958 0.438542 0.093750 0.064583
+28 0.751042 0.520833 0.114583 0.087500
diff --git a/ai/data/symbol/val/labels/symbol_00017.txt b/ai/data/symbol/val/labels/symbol_00017.txt
new file mode 100644
index 000000000..95e1b2866
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00017.txt
@@ -0,0 +1,15 @@
+28 0.335417 0.415625 0.066667 0.047917
+6 0.419792 0.416667 0.081250 0.058333
+32 0.495833 0.420833 0.079167 0.058333
+11 0.581250 0.423958 0.083333 0.056250
+21 0.670833 0.426042 0.087500 0.064583
+28 0.330208 0.485417 0.068750 0.041667
+6 0.416667 0.488542 0.079167 0.056250
+9 0.494792 0.495833 0.072917 0.058333
+11 0.578125 0.500000 0.081250 0.054167
+20 0.665625 0.507292 0.072917 0.043750
+12 0.317708 0.589583 0.077083 0.054167
+6 0.397917 0.602083 0.075000 0.050000
+20 0.484375 0.606250 0.089583 0.050000
+9 0.571875 0.612500 0.081250 0.054167
+28 0.667708 0.622917 0.089583 0.054167
diff --git a/ai/data/symbol/val/labels/symbol_00030.txt b/ai/data/symbol/val/labels/symbol_00030.txt
new file mode 100644
index 000000000..48989ca8c
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00030.txt
@@ -0,0 +1,5 @@
+1 0.391667 0.662500 0.100000 0.087500
+17 0.418750 0.581250 0.083333 0.066667
+9 0.437500 0.504167 0.079167 0.091667
+8 0.460417 0.402083 0.095833 0.095833
+23 0.492708 0.304167 0.081250 0.079167
diff --git a/ai/data/symbol/val/labels/symbol_00049.txt b/ai/data/symbol/val/labels/symbol_00049.txt
new file mode 100644
index 000000000..d9a83c5c7
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00049.txt
@@ -0,0 +1,4 @@
+30 0.170833 0.514583 0.129167 0.112500
+26 0.321875 0.465625 0.093750 0.143750
+21 0.478125 0.405208 0.131250 0.102083
+6 0.634375 0.354167 0.143750 0.100000
diff --git a/ai/data/symbol/val/labels/symbol_00052.txt b/ai/data/symbol/val/labels/symbol_00052.txt
new file mode 100644
index 000000000..600f8ca11
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00052.txt
@@ -0,0 +1,4 @@
+21 0.239583 0.221875 0.191667 0.114583
+14 0.457292 0.303125 0.210417 0.106250
+17 0.671875 0.393750 0.160417 0.141667
+29 0.884375 0.477083 0.189583 0.120833
diff --git a/ai/data/symbol/val/labels/symbol_00057.txt b/ai/data/symbol/val/labels/symbol_00057.txt
new file mode 100644
index 000000000..47612a12a
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00057.txt
@@ -0,0 +1,2 @@
+6 0.190625 0.193750 0.177083 0.141667
+9 0.083333 0.368750 0.162500 0.191667
diff --git a/ai/data/symbol/val/labels/symbol_00062.txt b/ai/data/symbol/val/labels/symbol_00062.txt
new file mode 100644
index 000000000..fc819282f
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00062.txt
@@ -0,0 +1,5 @@
+0 0.120833 0.769792 0.104167 0.114583
+17 0.267708 0.772917 0.114583 0.129167
+31 0.427083 0.770833 0.108333 0.125000
+31 0.587500 0.771875 0.141667 0.097917
+11 0.739583 0.766667 0.116667 0.133333
diff --git a/ai/data/symbol/val/labels/symbol_00077.txt b/ai/data/symbol/val/labels/symbol_00077.txt
new file mode 100644
index 000000000..0b259ea4f
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00077.txt
@@ -0,0 +1,4 @@
+12 0.213542 0.784375 0.085417 0.089583
+6 0.295833 0.792708 0.079167 0.102083
+34 0.367708 0.782292 0.060417 0.093750
+28 0.456250 0.776042 0.079167 0.097917
diff --git a/ai/data/symbol/val/labels/symbol_00079.txt b/ai/data/symbol/val/labels/symbol_00079.txt
new file mode 100644
index 000000000..be5f28001
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00079.txt
@@ -0,0 +1,6 @@
+29 0.113542 0.859375 0.077083 0.106250
+6 0.182292 0.863542 0.068750 0.110417
+34 0.243750 0.863542 0.066667 0.102083
+23 0.307292 0.867708 0.060417 0.093750
+42 0.370833 0.870833 0.075000 0.075000
+11 0.439583 0.870833 0.075000 0.108333
diff --git a/ai/data/symbol/val/labels/symbol_00097.txt b/ai/data/symbol/val/labels/symbol_00097.txt
new file mode 100644
index 000000000..a39e08afc
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00097.txt
@@ -0,0 +1,6 @@
+12 0.266667 0.478125 0.058333 0.081250
+6 0.332292 0.530208 0.060417 0.077083
+11 0.393750 0.567708 0.062500 0.060417
+33 0.457292 0.619792 0.064583 0.052083
+27 0.519792 0.659375 0.077083 0.068750
+13 0.585417 0.700000 0.070833 0.075000
diff --git a/ai/data/symbol/val/labels/symbol_00110.txt b/ai/data/symbol/val/labels/symbol_00110.txt
new file mode 100644
index 000000000..a0aece579
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00110.txt
@@ -0,0 +1,5 @@
+0 0.623958 0.218750 0.052083 0.062500
+17 0.640625 0.276042 0.056250 0.047917
+37 0.657292 0.321875 0.047917 0.052083
+34 0.669792 0.372917 0.060417 0.062500
+8 0.695833 0.425000 0.050000 0.054167
diff --git a/ai/data/symbol/val/labels/symbol_00112.txt b/ai/data/symbol/val/labels/symbol_00112.txt
new file mode 100644
index 000000000..3ca10b8dc
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00112.txt
@@ -0,0 +1,5 @@
+30 0.388542 0.413542 0.056250 0.060417
+6 0.461458 0.414583 0.056250 0.054167
+9 0.537500 0.414583 0.062500 0.062500
+8 0.610417 0.420833 0.075000 0.062500
+11 0.685417 0.418750 0.066667 0.058333
diff --git a/ai/data/symbol/val/labels/symbol_00116.txt b/ai/data/symbol/val/labels/symbol_00116.txt
new file mode 100644
index 000000000..c6f9eb6d9
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00116.txt
@@ -0,0 +1,4 @@
+23 0.376042 0.475000 0.097917 0.095833
+33 0.387500 0.389583 0.075000 0.079167
+17 0.382292 0.310417 0.089583 0.070833
+29 0.400000 0.217708 0.083333 0.093750
diff --git a/ai/data/symbol/val/labels/symbol_00123.txt b/ai/data/symbol/val/labels/symbol_00123.txt
new file mode 100644
index 000000000..4689e4f1c
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00123.txt
@@ -0,0 +1,5 @@
+37 0.229167 0.657292 0.154167 0.152083
+21 0.380208 0.604167 0.131250 0.137500
+11 0.531250 0.541667 0.150000 0.154167
+6 0.681250 0.482292 0.125000 0.185417
+26 0.821875 0.410417 0.127083 0.154167
diff --git a/ai/data/symbol/val/labels/symbol_00127.txt b/ai/data/symbol/val/labels/symbol_00127.txt
new file mode 100644
index 000000000..dfe8604f4
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00127.txt
@@ -0,0 +1,6 @@
+12 0.231250 0.270833 0.170833 0.166667
+17 0.428125 0.345833 0.168750 0.129167
+23 0.320833 0.462500 0.204167 0.154167
+34 0.628125 0.443750 0.247917 0.145833
+11 0.655208 0.589583 0.197917 0.154167
+28 0.889583 0.518750 0.158333 0.187500
diff --git a/ai/data/symbol/val/labels/symbol_00137.txt b/ai/data/symbol/val/labels/symbol_00137.txt
new file mode 100644
index 000000000..2f933f1a6
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00137.txt
@@ -0,0 +1,6 @@
+29 0.137500 0.537500 0.145833 0.125000
+6 0.291667 0.533333 0.150000 0.129167
+33 0.435417 0.533333 0.129167 0.116667
+8 0.567708 0.520833 0.135417 0.133333
+42 0.701042 0.514583 0.131250 0.095833
+23 0.833333 0.505208 0.125000 0.114583
diff --git a/ai/data/symbol/val/labels/symbol_00149.txt b/ai/data/symbol/val/labels/symbol_00149.txt
new file mode 100644
index 000000000..cc908ff63
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00149.txt
@@ -0,0 +1,5 @@
+29 0.306250 0.219792 0.170833 0.168750
+6 0.425000 0.350000 0.175000 0.179167
+34 0.543750 0.483333 0.162500 0.154167
+42 0.665625 0.601042 0.168750 0.135417
+11 0.780208 0.735417 0.197917 0.183333
diff --git a/ai/data/symbol/val/labels/symbol_00157.txt b/ai/data/symbol/val/labels/symbol_00157.txt
new file mode 100644
index 000000000..a4bee317f
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00157.txt
@@ -0,0 +1,5 @@
+29 0.150000 0.620833 0.129167 0.137500
+17 0.301042 0.619792 0.131250 0.147917
+9 0.447917 0.633333 0.133333 0.133333
+42 0.607292 0.631250 0.135417 0.079167
+23 0.758333 0.635417 0.120833 0.125000
diff --git a/ai/data/symbol/val/labels/symbol_00160.txt b/ai/data/symbol/val/labels/symbol_00160.txt
new file mode 100644
index 000000000..b3e89a3df
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00160.txt
@@ -0,0 +1,4 @@
+29 0.332292 0.239583 0.131250 0.137500
+17 0.347917 0.428125 0.125000 0.172917
+23 0.404167 0.592708 0.120833 0.114583
+14 0.456250 0.757292 0.112500 0.139583
diff --git a/ai/data/symbol/val/labels/symbol_00175.txt b/ai/data/symbol/val/labels/symbol_00175.txt
new file mode 100644
index 000000000..cf5e48fcd
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00175.txt
@@ -0,0 +1,5 @@
+29 0.455208 0.871875 0.181250 0.181250
+17 0.469792 0.684375 0.156250 0.168750
+34 0.468750 0.504167 0.145833 0.170833
+11 0.485417 0.329167 0.154167 0.141667
+8 0.490625 0.158333 0.160417 0.141667
diff --git a/ai/data/symbol/val/labels/symbol_00178.txt b/ai/data/symbol/val/labels/symbol_00178.txt
new file mode 100644
index 000000000..c7a08db77
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00178.txt
@@ -0,0 +1,4 @@
+29 0.633333 0.741667 0.095833 0.162500
+17 0.709375 0.806250 0.114583 0.158333
+23 0.841667 0.836458 0.116667 0.156250
+11 0.646875 0.948958 0.110417 0.102083
diff --git a/ai/data/symbol/val/labels/symbol_00179.txt b/ai/data/symbol/val/labels/symbol_00179.txt
new file mode 100644
index 000000000..dffcb389e
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00179.txt
@@ -0,0 +1,5 @@
+0 0.551042 0.559375 0.060417 0.052083
+9 0.602083 0.619792 0.075000 0.060417
+11 0.656250 0.675000 0.079167 0.062500
+6 0.705208 0.745833 0.068750 0.066667
+8 0.755208 0.805208 0.064583 0.056250
diff --git a/ai/data/symbol/val/labels/symbol_00187.txt b/ai/data/symbol/val/labels/symbol_00187.txt
new file mode 100644
index 000000000..8716d5730
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00187.txt
@@ -0,0 +1,15 @@
+29 0.332292 0.540625 0.064583 0.047917
+6 0.416667 0.536458 0.066667 0.047917
+33 0.488542 0.534375 0.064583 0.047917
+21 0.564583 0.533333 0.070833 0.054167
+11 0.641667 0.525000 0.070833 0.050000
+29 0.338542 0.616667 0.081250 0.058333
+29 0.351042 0.708333 0.072917 0.054167
+6 0.423958 0.612500 0.072917 0.050000
+6 0.431250 0.707292 0.062500 0.052083
+33 0.503125 0.605208 0.064583 0.052083
+33 0.589583 0.695833 0.075000 0.045833
+20 0.510417 0.698958 0.062500 0.052083
+20 0.575000 0.604167 0.066667 0.050000
+11 0.654167 0.600000 0.079167 0.054167
+8 0.672917 0.689583 0.075000 0.050000
diff --git a/ai/data/symbol/val/labels/symbol_00189.txt b/ai/data/symbol/val/labels/symbol_00189.txt
new file mode 100644
index 000000000..0c4db1a4f
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00189.txt
@@ -0,0 +1,15 @@
+26 0.135417 0.789583 0.037500 0.045833
+11 0.175000 0.782292 0.041667 0.047917
+31 0.216667 0.778125 0.033333 0.035417
+6 0.251042 0.783333 0.031250 0.041667
+12 0.288542 0.782292 0.035417 0.039583
+12 0.584375 0.779167 0.039583 0.050000
+12 0.869792 0.772917 0.039583 0.041667
+6 0.547917 0.780208 0.037500 0.043750
+6 0.830208 0.772917 0.035417 0.045833
+31 0.511458 0.779167 0.031250 0.041667
+31 0.794792 0.769792 0.035417 0.043750
+11 0.471875 0.779167 0.043750 0.045833
+11 0.757292 0.770833 0.039583 0.045833
+26 0.434375 0.780208 0.039583 0.039583
+26 0.716667 0.772917 0.045833 0.045833
diff --git a/ai/data/symbol/val/labels/symbol_00193.txt b/ai/data/symbol/val/labels/symbol_00193.txt
new file mode 100644
index 000000000..281256f76
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00193.txt
@@ -0,0 +1,5 @@
+6 0.359375 0.416667 0.072917 0.070833
+34 0.428125 0.420833 0.064583 0.070833
+26 0.495833 0.427083 0.062500 0.075000
+14 0.568750 0.434375 0.075000 0.072917
+23 0.641667 0.443750 0.066667 0.066667
diff --git a/ai/data/symbol/val/labels/symbol_00196.txt b/ai/data/symbol/val/labels/symbol_00196.txt
new file mode 100644
index 000000000..8994701c5
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00196.txt
@@ -0,0 +1,5 @@
+8 0.356250 0.338542 0.100000 0.114583
+25 0.432292 0.390625 0.093750 0.093750
+6 0.583333 0.491667 0.087500 0.091667
+31 0.498958 0.441667 0.081250 0.095833
+0 0.651042 0.558333 0.077083 0.091667
diff --git a/ai/data/symbol/val/labels/symbol_00199.txt b/ai/data/symbol/val/labels/symbol_00199.txt
new file mode 100644
index 000000000..ba38715e5
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00199.txt
@@ -0,0 +1,12 @@
+12 0.472917 0.251042 0.037500 0.043750
+12 0.478125 0.435417 0.043750 0.045833
+12 0.468750 0.633333 0.041667 0.041667
+17 0.472917 0.284375 0.045833 0.035417
+17 0.476042 0.473958 0.039583 0.031250
+17 0.468750 0.668750 0.041667 0.033333
+34 0.473958 0.320833 0.035417 0.037500
+34 0.479167 0.506250 0.045833 0.033333
+34 0.470833 0.705208 0.041667 0.031250
+21 0.475000 0.355208 0.041667 0.031250
+21 0.481250 0.541667 0.037500 0.037500
+21 0.470833 0.741667 0.037500 0.033333
diff --git a/ai/data/symbol/val/labels/symbol_00200.txt b/ai/data/symbol/val/labels/symbol_00200.txt
new file mode 100644
index 000000000..42fab9126
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00200.txt
@@ -0,0 +1,5 @@
+1 0.542708 0.203125 0.077083 0.060417
+6 0.601042 0.258333 0.064583 0.070833
+11 0.666667 0.307292 0.070833 0.077083
+33 0.735417 0.359375 0.070833 0.068750
+8 0.798958 0.407292 0.068750 0.060417
diff --git a/ai/data/symbol/val/labels/symbol_00206.txt b/ai/data/symbol/val/labels/symbol_00206.txt
new file mode 100644
index 000000000..1873ceb7c
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00206.txt
@@ -0,0 +1,12 @@
+26 0.621875 0.037500 0.043750 0.041667
+6 0.677083 0.056250 0.045833 0.041667
+11 0.740625 0.072917 0.056250 0.045833
+9 0.800000 0.090625 0.050000 0.047917
+12 0.861458 0.109375 0.052083 0.039583
+12 0.452083 0.432292 0.045833 0.027083
+12 0.460417 0.406250 0.041667 0.029167
+27 0.407292 0.430208 0.039583 0.022917
+27 0.415625 0.403125 0.027083 0.027083
+6 0.502083 0.434375 0.045833 0.031250
+11 0.548958 0.434375 0.047917 0.031250
+9 0.601042 0.438542 0.043750 0.031250
diff --git a/ai/data/symbol/val/labels/symbol_00207.txt b/ai/data/symbol/val/labels/symbol_00207.txt
new file mode 100644
index 000000000..edba9f784
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00207.txt
@@ -0,0 +1,10 @@
+28 0.176042 0.541667 0.072917 0.075000
+6 0.222917 0.592708 0.070833 0.068750
+33 0.262500 0.636458 0.079167 0.072917
+21 0.296875 0.682292 0.072917 0.068750
+11 0.333333 0.730208 0.070833 0.081250
+11 0.239583 0.817708 0.079167 0.064583
+20 0.198958 0.770833 0.077083 0.070833
+33 0.155208 0.730208 0.068750 0.060417
+6 0.110417 0.687500 0.075000 0.066667
+28 0.071875 0.635417 0.072917 0.066667
diff --git a/ai/data/symbol/val/labels/symbol_00211.txt b/ai/data/symbol/val/labels/symbol_00211.txt
new file mode 100644
index 000000000..9dc62a20b
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00211.txt
@@ -0,0 +1,22 @@
+27 0.391667 0.379167 0.062500 0.066667
+27 0.470833 0.470833 0.066667 0.066667
+27 0.610417 0.662500 0.062500 0.075000
+34 0.436458 0.355208 0.052083 0.068750
+34 0.506250 0.444792 0.070833 0.068750
+34 0.658333 0.630208 0.062500 0.060417
+34 0.722917 0.727083 0.070833 0.079167
+34 0.642708 0.542708 0.068750 0.077083
+37 0.503125 0.344792 0.068750 0.089583
+37 0.567708 0.429167 0.085417 0.087500
+37 0.706250 0.610417 0.062500 0.066667
+37 0.773958 0.697917 0.060417 0.070833
+17 0.532292 0.298958 0.060417 0.064583
+17 0.600000 0.385417 0.058333 0.066667
+17 0.683333 0.492708 0.062500 0.064583
+17 0.742708 0.573958 0.060417 0.056250
+17 0.806250 0.656250 0.058333 0.062500
+1 0.589583 0.281250 0.066667 0.079167
+1 0.663542 0.368750 0.081250 0.083333
+1 0.735417 0.460417 0.062500 0.087500
+1 0.793750 0.537500 0.079167 0.087500
+1 0.868750 0.635417 0.087500 0.104167
diff --git a/ai/data/symbol/val/labels/symbol_00213.txt b/ai/data/symbol/val/labels/symbol_00213.txt
new file mode 100644
index 000000000..2bdb1294e
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00213.txt
@@ -0,0 +1,3 @@
+29 0.430208 0.403125 0.085417 0.102083
+17 0.528125 0.396875 0.093750 0.127083
+42 0.639583 0.398958 0.112500 0.089583
diff --git a/ai/data/symbol/val/labels/symbol_00218.txt b/ai/data/symbol/val/labels/symbol_00218.txt
new file mode 100644
index 000000000..176bbaa3d
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00218.txt
@@ -0,0 +1,5 @@
+1 0.141667 0.418750 0.100000 0.133333
+17 0.256250 0.381250 0.116667 0.162500
+34 0.368750 0.367708 0.095833 0.110417
+8 0.475000 0.316667 0.091667 0.154167
+23 0.585417 0.310417 0.104167 0.137500
diff --git a/ai/data/symbol/val/labels/symbol_00228.txt b/ai/data/symbol/val/labels/symbol_00228.txt
new file mode 100644
index 000000000..5dbfb9a86
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00228.txt
@@ -0,0 +1,5 @@
+0 0.420312 0.377344 0.081250 0.039062
+6 0.499219 0.384375 0.057813 0.040625
+11 0.582031 0.386719 0.067187 0.039062
+34 0.666406 0.392969 0.067187 0.035937
+8 0.751563 0.394531 0.062500 0.032813
diff --git a/ai/data/symbol/val/labels/symbol_00243.txt b/ai/data/symbol/val/labels/symbol_00243.txt
new file mode 100644
index 000000000..255020924
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00243.txt
@@ -0,0 +1,4 @@
+12 0.353125 0.599219 0.093750 0.098437
+6 0.451562 0.588281 0.084375 0.076563
+33 0.539062 0.580469 0.075000 0.085938
+27 0.620313 0.567969 0.078125 0.089063
diff --git a/ai/data/symbol/val/labels/symbol_00245.txt b/ai/data/symbol/val/labels/symbol_00245.txt
new file mode 100644
index 000000000..7ff3f5192
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00245.txt
@@ -0,0 +1,18 @@
+0 0.271875 0.448437 0.068750 0.059375
+0 0.281250 0.506250 0.081250 0.043750
+0 0.284375 0.625000 0.065625 0.062500
+0 0.292187 0.689844 0.081250 0.076563
+6 0.356250 0.451562 0.075000 0.059375
+6 0.356250 0.510156 0.062500 0.054688
+6 0.364844 0.626563 0.079687 0.068750
+38 0.443750 0.450781 0.078125 0.060937
+38 0.428906 0.508594 0.076563 0.048438
+32 0.525000 0.455469 0.078125 0.057813
+32 0.511719 0.510938 0.089063 0.053125
+33 0.448437 0.629687 0.090625 0.062500
+26 0.534375 0.635156 0.084375 0.076563
+31 0.473438 0.701562 0.087500 0.062500
+26 0.567969 0.699219 0.085938 0.064062
+27 0.600000 0.458594 0.065625 0.051562
+27 0.590625 0.514062 0.056250 0.056250
+36 0.623437 0.632031 0.090625 0.067187
diff --git a/ai/data/symbol/val/labels/symbol_00286.txt b/ai/data/symbol/val/labels/symbol_00286.txt
new file mode 100644
index 000000000..1691d49a8
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00286.txt
@@ -0,0 +1,5 @@
+0 0.398438 0.539844 0.065625 0.045312
+6 0.473438 0.543750 0.062500 0.040625
+11 0.548438 0.550000 0.062500 0.046875
+34 0.623437 0.550000 0.068750 0.046875
+8 0.696875 0.552344 0.053125 0.039062
diff --git a/ai/data/symbol/val/labels/symbol_00287.txt b/ai/data/symbol/val/labels/symbol_00287.txt
new file mode 100644
index 000000000..3b7431065
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00287.txt
@@ -0,0 +1,10 @@
+0 0.375000 0.683594 0.075000 0.051562
+0 0.374219 0.735156 0.085938 0.045312
+6 0.471875 0.688281 0.078125 0.042188
+6 0.468750 0.735156 0.071875 0.039062
+11 0.551562 0.688281 0.059375 0.042188
+11 0.554688 0.735156 0.059375 0.045312
+34 0.635156 0.689063 0.076563 0.040625
+34 0.634375 0.733594 0.078125 0.042188
+8 0.723437 0.685937 0.065625 0.050000
+8 0.727344 0.732812 0.076563 0.040625
diff --git a/ai/data/symbol/val/labels/symbol_00288.txt b/ai/data/symbol/val/labels/symbol_00288.txt
new file mode 100644
index 000000000..acfd35518
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00288.txt
@@ -0,0 +1,5 @@
+1 0.425000 0.603906 0.056250 0.042188
+6 0.484375 0.614844 0.050000 0.048438
+37 0.542188 0.624219 0.050000 0.042188
+33 0.600000 0.635156 0.046875 0.032813
+8 0.660156 0.642969 0.051562 0.035937
diff --git a/ai/data/symbol/val/labels/symbol_00312.txt b/ai/data/symbol/val/labels/symbol_00312.txt
new file mode 100644
index 000000000..213f44740
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00312.txt
@@ -0,0 +1,5 @@
+29 0.597656 0.443750 0.060937 0.046875
+6 0.653906 0.458594 0.064062 0.045312
+11 0.716406 0.467187 0.064062 0.043750
+34 0.773438 0.477344 0.062500 0.039062
+27 0.825000 0.480469 0.050000 0.035937
diff --git a/ai/data/symbol/val/labels/symbol_00313.txt b/ai/data/symbol/val/labels/symbol_00313.txt
new file mode 100644
index 000000000..f8cab43c5
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00313.txt
@@ -0,0 +1,5 @@
+1 0.522656 0.707812 0.089063 0.068750
+6 0.577344 0.664062 0.082812 0.068750
+33 0.635156 0.617969 0.070312 0.073438
+8 0.692969 0.561719 0.085938 0.079687
+11 0.767969 0.500000 0.114062 0.112500
diff --git a/ai/data/symbol/val/labels/symbol_00317.txt b/ai/data/symbol/val/labels/symbol_00317.txt
new file mode 100644
index 000000000..139ffa7ad
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00317.txt
@@ -0,0 +1,5 @@
+1 0.337500 0.659375 0.093750 0.062500
+6 0.431250 0.660937 0.081250 0.065625
+11 0.522656 0.661719 0.079687 0.064062
+33 0.621094 0.660156 0.085938 0.057813
+8 0.707812 0.652344 0.075000 0.060937
diff --git a/ai/data/symbol/val/labels/symbol_00319.txt b/ai/data/symbol/val/labels/symbol_00319.txt
new file mode 100644
index 000000000..1934c3fbf
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00319.txt
@@ -0,0 +1,4 @@
+1 0.369792 0.363672 0.097917 0.069531
+6 0.468229 0.352734 0.094792 0.075781
+34 0.563542 0.345703 0.087500 0.069531
+27 0.644271 0.339062 0.071875 0.054688
diff --git a/ai/data/symbol/val/labels/symbol_00332.txt b/ai/data/symbol/val/labels/symbol_00332.txt
new file mode 100644
index 000000000..e3f037d04
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00332.txt
@@ -0,0 +1,5 @@
+8 0.417187 0.514062 0.065625 0.050000
+34 0.490625 0.514844 0.081250 0.051562
+37 0.558594 0.512500 0.060937 0.050000
+6 0.613281 0.512500 0.048438 0.040625
+0 0.667188 0.514844 0.065625 0.045312
diff --git a/ai/data/symbol/val/labels/symbol_00343.txt b/ai/data/symbol/val/labels/symbol_00343.txt
new file mode 100644
index 000000000..ed7f12445
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00343.txt
@@ -0,0 +1,19 @@
+30 0.294531 0.178906 0.039062 0.048438
+30 0.296875 0.226562 0.025000 0.050000
+30 0.303125 0.272656 0.040625 0.045312
+30 0.308594 0.317969 0.029687 0.045312
+6 0.347656 0.312500 0.032813 0.037500
+6 0.342187 0.269531 0.031250 0.042188
+6 0.332813 0.171094 0.034375 0.045312
+11 0.368750 0.163281 0.031250 0.045312
+11 0.376563 0.260937 0.034375 0.043750
+11 0.458594 0.297656 0.032813 0.039062
+34 0.383594 0.306250 0.032813 0.034375
+34 0.410156 0.154688 0.045312 0.046875
+34 0.414062 0.253906 0.040625 0.039062
+8 0.410156 0.206250 0.039062 0.046875
+8 0.454688 0.148438 0.034375 0.046875
+8 0.457031 0.246875 0.042188 0.037500
+14 0.453906 0.201563 0.045312 0.040625
+31 0.367969 0.214844 0.042188 0.039062
+8 0.421875 0.302344 0.034375 0.045312
diff --git a/ai/data/symbol/val/labels/symbol_00359.txt b/ai/data/symbol/val/labels/symbol_00359.txt
new file mode 100644
index 000000000..7f8cfa1a8
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00359.txt
@@ -0,0 +1,24 @@
+1 0.301563 0.208594 0.059375 0.070312
+1 0.332813 0.256250 0.056250 0.065625
+1 0.376563 0.342969 0.059375 0.045312
+1 0.350000 0.305469 0.050000 0.051562
+1 0.396094 0.386719 0.057813 0.060937
+6 0.347656 0.185156 0.048438 0.048438
+6 0.376563 0.232813 0.046875 0.056250
+6 0.389844 0.279687 0.048438 0.053125
+6 0.419531 0.322656 0.042188 0.042188
+6 0.442969 0.360156 0.048438 0.051562
+38 0.484375 0.334375 0.056250 0.056250
+38 0.455469 0.303125 0.042188 0.037500
+38 0.417187 0.211719 0.053125 0.045312
+38 0.393750 0.165625 0.056250 0.046875
+36 0.531250 0.202344 0.059375 0.060937
+34 0.436719 0.136719 0.054688 0.057813
+34 0.460156 0.182031 0.060937 0.051562
+34 0.489063 0.275781 0.050000 0.042188
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+8 0.483594 0.111719 0.054688 0.054688
+8 0.507812 0.158594 0.056250 0.048438
+8 0.535156 0.256250 0.048438 0.046875
+8 0.575000 0.285156 0.050000 0.048438
+34 0.434375 0.245312 0.059375 0.053125
diff --git a/ai/data/symbol/val/labels/symbol_00361.txt b/ai/data/symbol/val/labels/symbol_00361.txt
new file mode 100644
index 000000000..4190ad617
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00361.txt
@@ -0,0 +1,4 @@
+9 0.610156 0.478906 0.054688 0.039062
+37 0.671875 0.482031 0.050000 0.032813
+17 0.719531 0.485156 0.039062 0.032813
+0 0.775781 0.487500 0.054688 0.031250
diff --git a/ai/data/symbol/val/labels/symbol_00370.txt b/ai/data/symbol/val/labels/symbol_00370.txt
new file mode 100644
index 000000000..c42332bd6
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00370.txt
@@ -0,0 +1,5 @@
+3 0.316406 0.632031 0.057813 0.051562
+34 0.389062 0.631250 0.062500 0.046875
+6 0.454688 0.626563 0.040625 0.046875
+27 0.510156 0.624219 0.048438 0.048438
+37 0.575000 0.620313 0.062500 0.053125
diff --git a/ai/data/symbol/val/labels/symbol_00372.txt b/ai/data/symbol/val/labels/symbol_00372.txt
new file mode 100644
index 000000000..eaef3669f
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00372.txt
@@ -0,0 +1,5 @@
+12 0.129688 0.414844 0.090625 0.082812
+6 0.221875 0.375781 0.084375 0.089063
+11 0.303906 0.321094 0.092188 0.082812
+33 0.394531 0.272656 0.076563 0.085938
+27 0.471875 0.218750 0.068750 0.068750
diff --git a/ai/data/symbol/val/labels/symbol_00391.txt b/ai/data/symbol/val/labels/symbol_00391.txt
new file mode 100644
index 000000000..b575a31e5
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00391.txt
@@ -0,0 +1,5 @@
+1 0.429688 0.567187 0.075000 0.059375
+6 0.506250 0.567187 0.062500 0.062500
+33 0.575000 0.563281 0.068750 0.064062
+27 0.642969 0.562500 0.060937 0.056250
+36 0.714844 0.560937 0.073438 0.068750
diff --git a/ai/data/symbol/val/labels/symbol_00402.txt b/ai/data/symbol/val/labels/symbol_00402.txt
new file mode 100644
index 000000000..a2bc546db
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00402.txt
@@ -0,0 +1,5 @@
+1 0.337500 0.378906 0.093750 0.070312
+6 0.417187 0.428125 0.078125 0.062500
+11 0.485938 0.483594 0.078125 0.076563
+33 0.570312 0.531250 0.090625 0.084375
+8 0.642188 0.591406 0.087500 0.082812
diff --git a/ai/data/symbol/val/labels/symbol_00415.txt b/ai/data/symbol/val/labels/symbol_00415.txt
new file mode 100644
index 000000000..897576ca7
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00415.txt
@@ -0,0 +1,5 @@
+1 0.379688 0.121094 0.062500 0.045312
+6 0.433594 0.121094 0.045312 0.039062
+38 0.487500 0.121094 0.040625 0.039062
+33 0.542969 0.129688 0.045312 0.037500
+8 0.585156 0.140625 0.035937 0.034375
diff --git a/ai/data/symbol/val/labels/symbol_00420.txt b/ai/data/symbol/val/labels/symbol_00420.txt
new file mode 100644
index 000000000..875d50d70
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00420.txt
@@ -0,0 +1,5 @@
+1 0.276173 0.534714 0.123947 0.129111
+6 0.429603 0.512180 0.139591 0.130329
+11 0.562575 0.478685 0.162455 0.126675
+34 0.735860 0.453715 0.155235 0.115713
+27 0.879663 0.423264 0.113117 0.084044
diff --git a/ai/data/symbol/val/labels/symbol_00430.txt b/ai/data/symbol/val/labels/symbol_00430.txt
new file mode 100644
index 000000000..49f4ae883
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00430.txt
@@ -0,0 +1,5 @@
+0 0.528125 0.591406 0.040625 0.048438
+6 0.577344 0.571094 0.051562 0.042188
+38 0.619531 0.539844 0.048438 0.048438
+33 0.667969 0.508594 0.042188 0.042188
+8 0.707031 0.482031 0.042188 0.042188
diff --git a/ai/data/symbol/val/labels/symbol_00432.txt b/ai/data/symbol/val/labels/symbol_00432.txt
new file mode 100644
index 000000000..f8394eb82
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00432.txt
@@ -0,0 +1,5 @@
+0 0.471875 0.621094 0.068750 0.048438
+6 0.546875 0.629687 0.062500 0.050000
+11 0.613281 0.639844 0.060937 0.048438
+9 0.495312 0.678906 0.068750 0.048438
+8 0.570312 0.685937 0.065625 0.050000
diff --git a/ai/data/symbol/val/labels/symbol_00456.txt b/ai/data/symbol/val/labels/symbol_00456.txt
new file mode 100644
index 000000000..32cf0f799
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00456.txt
@@ -0,0 +1,5 @@
+2 0.267188 0.726562 0.096875 0.078125
+3 0.357812 0.735938 0.084375 0.071875
+3 0.446094 0.743750 0.082812 0.075000
+2 0.529687 0.749219 0.087500 0.076563
+4 0.626563 0.753906 0.100000 0.070312
diff --git a/ai/data/symbol/val/labels/symbol_00457.txt b/ai/data/symbol/val/labels/symbol_00457.txt
new file mode 100644
index 000000000..dcb7391d1
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00457.txt
@@ -0,0 +1,5 @@
+2 0.304688 0.614062 0.081250 0.068750
+3 0.388281 0.617188 0.076563 0.056250
+3 0.469531 0.620313 0.076563 0.062500
+2 0.552344 0.619531 0.076563 0.064062
+4 0.639844 0.621875 0.089063 0.065625
diff --git a/ai/data/symbol/val/labels/symbol_00475.txt b/ai/data/symbol/val/labels/symbol_00475.txt
new file mode 100644
index 000000000..74c7b1d5b
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00475.txt
@@ -0,0 +1,4 @@
+1 0.553906 0.303906 0.129688 0.145313
+6 0.658594 0.403125 0.095312 0.171875
+33 0.778125 0.499219 0.125000 0.179688
+8 0.905469 0.572656 0.107813 0.148438
diff --git a/ai/data/symbol/val/labels/symbol_00497.txt b/ai/data/symbol/val/labels/symbol_00497.txt
new file mode 100644
index 000000000..11c435edf
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00497.txt
@@ -0,0 +1,15 @@
+1 0.328000 0.178000 0.108000 0.064000
+1 0.324000 0.484000 0.112000 0.072000
+1 0.334000 0.736000 0.108000 0.064000
+6 0.435000 0.184000 0.090000 0.068000
+6 0.431000 0.474000 0.094000 0.072000
+6 0.439000 0.737000 0.086000 0.066000
+11 0.531000 0.188000 0.094000 0.076000
+11 0.527000 0.464000 0.086000 0.076000
+11 0.535000 0.741000 0.090000 0.066000
+32 0.630000 0.203000 0.084000 0.050000
+32 0.620000 0.465000 0.084000 0.058000
+32 0.626000 0.754000 0.088000 0.052000
+27 0.712000 0.202000 0.072000 0.056000
+27 0.705000 0.455000 0.074000 0.062000
+27 0.713000 0.758000 0.070000 0.060000
diff --git a/ai/data/symbol/val/labels/symbol_00514.txt b/ai/data/symbol/val/labels/symbol_00514.txt
new file mode 100644
index 000000000..6f40fe52a
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00514.txt
@@ -0,0 +1,5 @@
+1 0.441406 0.320312 0.054688 0.046875
+6 0.498437 0.332813 0.062500 0.053125
+11 0.557813 0.344531 0.056250 0.054688
+33 0.618750 0.353906 0.053125 0.054688
+8 0.681250 0.363281 0.062500 0.051562
diff --git a/ai/data/symbol/val/labels/symbol_00518.txt b/ai/data/symbol/val/labels/symbol_00518.txt
new file mode 100644
index 000000000..866b56c62
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00518.txt
@@ -0,0 +1,5 @@
+29 0.308594 0.506250 0.079687 0.093750
+6 0.378125 0.515625 0.065625 0.093750
+34 0.452344 0.524219 0.070312 0.092188
+27 0.520312 0.532031 0.056250 0.089063
+11 0.586719 0.534375 0.067187 0.096875
diff --git a/ai/data/symbol/val/labels/symbol_00521.txt b/ai/data/symbol/val/labels/symbol_00521.txt
new file mode 100644
index 000000000..b5d7ade1e
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00521.txt
@@ -0,0 +1,6 @@
+29 0.381667 0.380000 0.076667 0.090000
+11 0.376667 0.482500 0.080000 0.095000
+6 0.498333 0.385000 0.070000 0.090000
+25 0.490000 0.485000 0.073333 0.100000
+14 0.616667 0.390000 0.100000 0.110000
+9 0.611667 0.495000 0.090000 0.100000
diff --git a/ai/data/symbol/val/labels/symbol_00528.txt b/ai/data/symbol/val/labels/symbol_00528.txt
new file mode 100644
index 000000000..774a63649
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00528.txt
@@ -0,0 +1,5 @@
+0 0.208594 0.815625 0.164062 0.231250
+6 0.390625 0.786719 0.162500 0.245312
+37 0.556250 0.711719 0.168750 0.245312
+34 0.729688 0.638281 0.156250 0.273438
+27 0.864844 0.538281 0.114062 0.192188
diff --git a/ai/data/symbol/val/labels/symbol_00532.txt b/ai/data/symbol/val/labels/symbol_00532.txt
new file mode 100644
index 000000000..a9311ad89
--- /dev/null
+++ b/ai/data/symbol/val/labels/symbol_00532.txt
@@ -0,0 +1,5 @@
+1 0.228906 0.614844 0.117188 0.117188
+6 0.331250 0.663281 0.087500 0.120313
+33 0.438281 0.699219 0.107813 0.123438
+8 0.550781 0.728125 0.082812 0.118750
+37 0.653125 0.750781 0.100000 0.114062
diff --git a/ai/evaluate_model.py b/ai/evaluate_model.py
new file mode 100644
index 000000000..fad8821f2
--- /dev/null
+++ b/ai/evaluate_model.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+import os
+import json
+import time
+import yaml
+import logging
+import numpy as np
+from PIL import Image, ImageOps
+from collections import defaultdict
+from ultralytics import YOLO
+import torch
+
+# ───── 로깅 설정 ─────
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# ───── 경로 설정 ─────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+PERF_ROOT = os.path.join(BASE_DIR, "..", "front", "fe-rw", "public", "performance")
+
+STAIN_MODEL_PATH = os.path.join(BASE_DIR, "stain", "stain_cls.pt")
+SYMBOL_MODEL_PATH = os.path.join(BASE_DIR, "symbol", "laundry_labels_cls.pt")
+STAIN_DATA_YAML = os.path.join(BASE_DIR, "data", "stain", "data.yaml")
+SYMBOL_DATA_YAML = os.path.join(BASE_DIR, "data", "symbol", "data.yaml")
+TEST_STAIN_IMG_DIR = os.path.join(BASE_DIR, "data", "stain", "test", "images")
+
+CLASS_NAMES_STAIN = [
+    "blood",
+    "coffee",
+    "earth",
+    "ink",
+    "kimchi",
+    "lipstick",
+    "mustard",
+    "oil",
+    "wine",
+]
+CLASS_CONF_THRESH = {
+    "blood": 0.260,
+    "coffee": 0.350,
+    "earth": 0.230,
+    "ink": 0.190,
+    "kimchi": 0.500,
+    "lipstick": 0.330,
+    "mustard": 0.160,
+    "oil": 0.360,
+    "wine": 0.100,
+}
+GLOBAL_CONF = min(CLASS_CONF_THRESH.values())
+EVAL_STAIN_SIZE = 320
+
+SYMBOL_CONF = 0.1
+SYMBOL_AUG = True
+
+
+def load_yolo(weights):
+    try:
+        model = YOLO(weights).to("cuda:0")
+        model.fuse()
+        return model.to("cuda:0", dtype=torch.float16), "cuda:0"
+    except:
+        return YOLO(weights), "cpu"
+
+
+def evaluate_stain(weights_path):
+    logger.info(f"📊 Evaluating stain model: {weights_path}")
+    model, device = load_yolo(weights_path)
+
+    def resize_pad(im):
+        w, h = im.size
+        m = max(w, h)
+        pad = ((m - w) // 2, (m - h) // 2, m - w - (m - w) // 2, m - h - (m - h) // 2)
+        return ImageOps.expand(im, pad, fill=(0, 0, 0)).resize((EVAL_STAIN_SIZE,) * 2)
+
+    stats = {
+        "s": defaultdict(int),
+        "m": defaultdict(int),
+        "t1": defaultdict(int),
+        "t3": defaultdict(int),
+    }
+    inf_t = 0.0
+
+    for fn in os.listdir(TEST_STAIN_IMG_DIR):
+        if not fn.lower().endswith((".jpg", ".png")):
+            continue
+        gt = fn.split("_")[0].lower()
+        if gt not in CLASS_NAMES_STAIN:
+            continue
+        idx = CLASS_NAMES_STAIN.index(gt)
+        stats["s"][idx] += 1
+
+        img = Image.open(os.path.join(TEST_STAIN_IMG_DIR, fn)).convert("RGB")
+        img = resize_pad(img)
+        t0 = time.time()
+        res = model(img, conf=GLOBAL_CONF, device=device)[0]
+        inf_t += time.time() - t0
+
+        if not res.boxes:
+            stats["m"][idx] += 1
+            continue
+
+        cls = res.boxes.cls.cpu().numpy().astype(int)
+        conf = res.boxes.conf.cpu().numpy()
+        keep = np.array(
+            [
+                conf[i] >= CLASS_CONF_THRESH[CLASS_NAMES_STAIN[c]]
+                for i, c in enumerate(cls)
+            ],
+            dtype=bool,
+        )
+        cls, conf = cls[keep], conf[keep]
+        if cls.size == 0:
+            stats["m"][idx] += 1
+            continue
+
+        order = conf.argsort()[::-1]
+        top3 = cls[order[:3]]
+        if idx == top3[0]:
+            stats["t1"][idx] += 1
+        if idx in top3:
+            stats["t3"][idx] += 1
+
+    per = {}
+    tot_s = tot_m = tot1 = tot3 = 0
+    for i, name in enumerate(CLASS_NAMES_STAIN):
+        s = stats["s"][i]
+        m = stats["m"][i]
+        non_miss = s - m
+        o1 = stats["t1"][i]
+        o3 = stats["t3"][i]
+        if non_miss > 0:
+            per[name] = {
+                "samples": s,
+                "miss": m,
+                "top1_acc": round(o1 / non_miss, 4),
+                "top3_acc": round(o3 / non_miss, 4),
+            }
+            tot_s += s
+            tot_m += m
+            tot1 += o1
+            tot3 += o3
+
+    overall = {}
+    total_non_miss = tot_s - tot_m
+    if total_non_miss > 0:
+        overall = {
+            "samples": tot_s,
+            "miss": tot_m,
+            "top1_acc": round(tot1 / total_non_miss, 4),
+            "top3_acc": round(tot3 / total_non_miss, 4),
+            "precision": round(tot1 / total_non_miss, 4),
+            "recall": round(tot1 / total_non_miss, 4),
+            "inference_time": {
+                "total_s": round(inf_t, 4),
+                "avg_per_image_s": round(inf_t / tot_s, 4),
+            },
+        }
+
+    return {"per_class": per, "overall": overall}
+
+
+def evaluate_symbol(weights_path):
+    logger.info(f"📊 Evaluating symbol model: {weights_path}")
+    model, device = load_yolo(weights_path)
+
+    first = model.val(
+        data=SYMBOL_DATA_YAML,
+        split="test",
+        imgsz=2048,
+        conf=SYMBOL_CONF,
+        augment=False,
+        device=device,
+        verbose=False,
+    )
+    initial_maps = dict(zip(first.ap_class_index, first.maps))
+    valid_ids = [int(idx) for idx, ap in initial_maps.items() if ap > 0.0]
+
+    final = model.val(
+        data=SYMBOL_DATA_YAML,
+        split="test",
+        imgsz=2048,
+        conf=SYMBOL_CONF,
+        augment=SYMBOL_AUG,
+        classes=valid_ids,
+        device=device,
+        verbose=False,
+    )
+    P, R, mAP50, mAP5095 = final.box.mean_results()
+    inf_ms = final.speed.get("inference", 0.0)
+    names = yaml.safe_load(open(SYMBOL_DATA_YAML))["names"]
+    ap_map = {int(idx): float(ap) for idx, ap in zip(final.ap_class_index, final.maps)}
+    per_cls = {name: ap_map.get(i, 0.0) for i, name in enumerate(names)}
+
+    return {
+        "precision": P,
+        "recall": R,
+        "mAP50": mAP50,
+        "mAP50-95": mAP5095,
+        "inference_time_ms": inf_ms,
+        "per_class": per_cls,
+    }
+
+
+def run_evaluation_only():
+    os.makedirs(os.path.join(PERF_ROOT, "stain"), exist_ok=True)
+    os.makedirs(os.path.join(PERF_ROOT, "symbol"), exist_ok=True)
+
+    if os.path.exists(STAIN_MODEL_PATH):
+        stain_result = evaluate_stain(STAIN_MODEL_PATH)
+        with open(
+            os.path.join(PERF_ROOT, "stain", "performance.json"), "w", encoding="utf8"
+        ) as f:
+            json.dump(
+                {
+                    "model_type": "stain",
+                    "weights_path": STAIN_MODEL_PATH,
+                    "metrics": stain_result,
+                },
+                f,
+                indent=2,
+                ensure_ascii=False,
+            )
+        logger.info("✅ Stain evaluation saved.")
+
+    if os.path.exists(SYMBOL_MODEL_PATH):
+        symbol_result = evaluate_symbol(SYMBOL_MODEL_PATH)
+        with open(
+            os.path.join(PERF_ROOT, "symbol", "performance.json"), "w", encoding="utf8"
+        ) as f:
+            json.dump(
+                {
+                    "model_type": "symbol",
+                    "weights_path": SYMBOL_MODEL_PATH,
+                    "metrics": symbol_result,
+                },
+                f,
+                indent=2,
+                ensure_ascii=False,
+            )
+        logger.info("✅ Symbol evaluation saved.")
+
+
+if __name__ == "__main__":
+    logger.info("📌 Evaluation Only Mode Started")
+    run_evaluation_only()
+    logger.info("✅ Evaluation Only Mode Completed")
diff --git a/ai/main.py b/ai/main.py
new file mode 100644
index 000000000..ac8d11098
--- /dev/null
+++ b/ai/main.py
@@ -0,0 +1,346 @@
+import os
+import json
+import cv2
+import numpy as np
+from pathlib import Path
+from ultralytics import YOLO
+import torch
+import sys
+import random
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ───── 경로 설정 ─────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+STAIN_MODEL_PATH = os.path.join(BASE_DIR, "stain", "stain_cls.pt")
+LABEL_MODEL_PATH = os.path.join(BASE_DIR, "symbol", "laundry_labels_cls.pt")
+STAIN_GUIDE_PATH = os.path.join(BASE_DIR, "stain", "stain_washing_guidelines.json")
+LABEL_GUIDE_PATH = os.path.join(BASE_DIR, "symbol", "label_symbol_guide.json")
+OUT_DIR = os.path.abspath(os.path.join(BASE_DIR, "..", "images"))
+
+# 로컬 LLM 모델 경로 설정 (사전에 다운로드된 모델 디렉토리)
+LLM_MODEL_DIR = os.path.join(BASE_DIR, "llm/kanana-nano-2.1b-base")
+# LLM 모델 로딩
+llm_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_DIR, padding_side="left")
+llm_tokenizer.pad_token = llm_tokenizer.eos_token
+
+try:
+    llm_model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_DIR, torch_dtype=torch.bfloat16, trust_remote_code=True
+    ).to("cuda")
+except (RuntimeError, ValueError) as e:
+    print("⚠️ bfloat16 미지원 → float32로 재시도")
+    llm_model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_DIR, torch_dtype=torch.float32, trust_remote_code=True
+    ).to("cuda")
+
+
+# ───── 클래스 및 설정 ─────
+STAIN_CLASSES = [
+    "blood",
+    "coffee",
+    "earth",
+    "ink",
+    "kimchi",
+    "lipstick",
+    "mustard",
+    "oil",
+    "wine",
+]
+CLASS_CONF_TH = {
+    "blood": 0.260,
+    "coffee": 0.350,
+    "earth": 0.230,
+    "ink": 0.190,
+    "kimchi": 0.500,
+    "lipstick": 0.330,
+    "mustard": 0.160,
+    "oil": 0.360,
+    "wine": 0.100,
+}
+GLOBAL_CONF = min(CLASS_CONF_TH.values())
+LABEL_CONF = 0.5
+TOP_K = 3
+
+
+# ─── LLM 프롬프트 구성 ───
+def build_llm_prompt(stain_class, stain_advice, label_expls):
+    if not label_expls:
+        label_text = "세탁 기호가 감지되지 않았습니다."
+    else:
+        label_text = ", ".join(label_expls)
+
+    return (
+        f"아래는 옷에 묻은 얼룩과 해당 세탁 기호에 대한 설명입니다.\n"
+        f"- 얼룩 종류는 {stain_class}이며,\n"
+        f"- 해당 얼룩에 대해 추천되는 세탁법은 다음과 같습니다: {stain_advice}\n"
+        f"- 세탁 기호는 다음과 같습니다: {label_text}\n\n"
+        "이 정보를 바탕으로, 옷을 어떻게 세탁해야 하는지 부드럽고 자연스러운 한 문단으로 설명하세요. "
+        "사용자에게 말하듯 쓰되, 인사말 없이 직접적인 명령형으로 작성하세요.\n\n세탁 방법:"
+    )
+
+
+# ───── 모델 및 가이드 로딩 ─────
+stain_model = YOLO(STAIN_MODEL_PATH)
+label_model = YOLO(LABEL_MODEL_PATH)
+with open(STAIN_GUIDE_PATH, "r", encoding="utf-8") as f:
+    stain_guide = json.load(f)
+with open(LABEL_GUIDE_PATH, "r", encoding="utf-8") as f:
+    label_guide = json.load(f)
+
+
+# ───── stain 예측 ─────
+def predict_stain(image_path):
+    result = stain_model(image_path, conf=GLOBAL_CONF, imgsz=320)[0]
+    if result.boxes is None or len(result.boxes) == 0:
+        return None, None
+
+    classes = result.boxes.cls.cpu().numpy().astype(int)
+    probs = result.boxes.conf.cpu().numpy()
+    boxes = result.boxes.xyxy.cpu().numpy()
+
+    keep = np.array(
+        [
+            probs[i] >= CLASS_CONF_TH[STAIN_CLASSES[cls]]
+            for i, cls in enumerate(classes)
+        ],
+        dtype=bool,
+    )
+
+    if not keep.any():
+        return None, None
+    classes, probs, boxes = classes[keep], probs[keep], boxes[keep]
+    if len(classes) == 0:
+        return [], ""
+
+    sorted_idx = probs.argsort()[::-1]
+    top3 = [
+        (STAIN_CLASSES[classes[i]], round(float(probs[i]), 3))
+        for i in sorted_idx[:TOP_K]
+    ]
+    top1_idx = sorted_idx[0]
+
+    # 시각화
+    img = cv2.imread(image_path)
+    h, w = img.shape[:2]
+    for i in range(len(classes)):
+        x1, y1, x2, y2 = map(int, boxes[i])
+        cls_id = classes[i]
+        label = f"{STAIN_CLASSES[cls_id]} {probs[i]:.2f}"
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
+        cv2.putText(
+            img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2
+        )
+
+    name = Path(image_path).stem
+    out_dir = os.path.join(OUT_DIR, "output", "stain")
+    os.makedirs(os.path.join(out_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(out_dir, "labels"), exist_ok=True)
+
+    image_out = os.path.join(out_dir, "images", f"{name}_stain.jpg")
+    label_out = os.path.join(out_dir, "labels", f"{name}.txt")
+
+    # Top-1 라벨 저장
+    with open(label_out, "w") as f:
+        i = top1_idx
+        x1, y1, x2, y2 = map(int, boxes[i])
+        xc = ((x1 + x2) / 2) / w
+        yc = ((y1 + y2) / 2) / h
+        bw = (x2 - x1) / w
+        bh = (y2 - y1) / h
+        f.write(f"{classes[i]} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}\n")
+
+    cv2.imwrite(image_out, img)
+    return top3, os.path.relpath(image_out, os.path.dirname(OUT_DIR)).replace("\\", "/")
+
+
+# ───── symbol 예측 ─────
+def predict_label(image_path):
+    result = label_model.predict(
+        source=image_path,
+        imgsz=2048,
+        conf=LABEL_CONF,
+        iou=0.35,
+        max_det=1000,
+        agnostic_nms=True,
+        augment=True,
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        verbose=False,
+    )[0]
+
+    if result.boxes is None or len(result.boxes) == 0:
+        return None, None
+    boxes = result.boxes.xyxy.cpu().numpy()
+    classes = result.boxes.cls.cpu().numpy().astype(int)
+    probs = result.boxes.conf.cpu().numpy()
+
+    img = cv2.imread(image_path)
+    h, w = img.shape[:2]
+    name = Path(image_path).stem
+
+    out_dir = os.path.join(OUT_DIR, "output", "symbol")
+    os.makedirs(os.path.join(out_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(out_dir, "labels"), exist_ok=True)
+
+    image_out = os.path.join(out_dir, "images", f"{name}_symbol.jpg")
+    label_out = os.path.join(out_dir, "labels", f"{name}.txt")
+
+    with open(label_out, "w") as f:
+        for cls_id, conf, box in zip(classes, probs, boxes):
+            x1, y1, x2, y2 = map(int, box)
+            class_name = label_model.names[cls_id]
+            label = f"{class_name} {conf:.2f}"
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(
+                img, label, (x1, y1 - 20), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), 4
+            )
+
+            xc = ((x1 + x2) / 2) / w
+            yc = ((y1 + y2) / 2) / h
+            bw = (x2 - x1) / w
+            bh = (y2 - y1) / h
+            f.write(f"{cls_id} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}\n")
+
+    cv2.imwrite(image_out, img)
+    label_names = list(set(label_model.names[cls] for cls in classes))
+    return label_names, os.path.relpath(image_out, os.path.dirname(OUT_DIR)).replace(
+        "\\", "/"
+    )
+
+
+# ───── 실행 진입점 ─────
+def main():
+    if len(sys.argv) < 3:
+        print("사용법: python laundry_pipeline.py <analysis_type> <image_path>")
+        sys.exit(1)
+
+    analysis_type = sys.argv[1]
+    image_path = sys.argv[2]
+
+    if analysis_type == "stain_only":
+        top3, output_path = predict_stain(image_path)
+        if top3 is None:
+            output = {
+                "detected_stain": {
+                    "top3": [{"class": "", "confidence": ""} for _ in range(3)]
+                },
+                "washing_instructions": [{"class": "", "instructions": ["", "", ""]}],
+                "output_image_path": "",
+            }
+        else:
+            output = {
+                "detected_stain": {
+                    "top3": [{"class": c, "confidence": s} for c, s in top3]
+                },
+                "washing_instructions": [],
+                "output_image_path": output_path,
+            }
+
+            seen_classes = set()
+
+            for stain, _ in top3:
+                if stain in seen_classes:
+                    continue  # 중복 방지
+                seen_classes.add(stain)
+
+                methods = stain_guide.get(stain, [])
+                if isinstance(methods, list) and len(methods) > 0:
+                    first = methods[0]
+                    remaining = methods[1:]
+                    rand = random.sample(remaining, k=min(2, len(remaining)))
+                    combined = [first] + rand
+                else:
+                    combined = ["정보 없음"]
+
+                output["washing_instructions"].append(
+                    {"class": stain, "instructions": combined}
+                )
+
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    elif analysis_type == "label_only":
+        labels, output_path = predict_label(image_path)
+        if labels is None:
+            output = {
+                "detected_labels": [],
+                "label_explanation": [],
+                "output_image_path": "",
+            }
+        else:
+            output = {
+                "detected_labels": labels,
+                "label_explanation": [
+                    label_guide.get(lbl, "정보 없음") for lbl in labels
+                ],
+                "output_image_path": output_path,
+            }
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    elif analysis_type == "stain_and_label":
+        if len(sys.argv) < 4:
+            print(
+                "⚠️ stain_and_label 분석에는 이미지 2장(stain, label) 경로가 필요합니다."
+            )
+            sys.exit(1)
+
+        stain_img = sys.argv[2]
+        label_img = sys.argv[3]
+
+        top3, stain_out = predict_stain(stain_img)
+        labels, label_out = predict_label(label_img)
+        if top3 is None:
+            stain_class = ""
+            stain_advices = ""
+        else:
+            stain_class = top3[0][0]
+            stain_advices = stain_guide.get(stain_class, [""])[0]
+
+        label_expls = [label_guide.get(lbl, "") for lbl in labels] if labels else []
+
+        # DN_wash가 포함되어 있다면 고정된 멘트 출력
+        if any("세탁 금지" in expl for expl in label_expls):
+            llm_output = "감지된 세탁 기호에 따라 물세탁이 불가하여 가정에서 얼룩 제거가 어려운 제품입니다. 반드시 전문 세탁소에 의뢰하시기 바랍니다."
+        elif not stain_class:
+            llm_output = ""
+        else:
+            prompt = build_llm_prompt(stain_class, stain_advices, label_expls)
+            input_ids = llm_tokenizer(prompt, return_tensors="pt").to("cuda")[
+                "input_ids"
+            ]
+            with torch.no_grad():
+                output = llm_model.generate(
+                    input_ids,
+                    max_new_tokens=256,
+                    do_sample=True,
+                    temperature=0.6,
+                    top_p=0.85,
+                    top_k=30,
+                    pad_token_id=llm_tokenizer.eos_token_id,
+                )
+            decoded = llm_tokenizer.decode(output[0], skip_special_tokens=True)
+            llm_output = (
+                decoded.split("세탁 방법:")[-1].strip()
+                if "세탁 방법:" in decoded
+                else decoded.strip()
+            )
+
+        output = {
+            "top1_stain": stain_class,
+            "washing_instructions": stain_advices,
+            "detected_labels": labels if labels else [],
+            "label_explanation": label_expls,
+            "output_image_paths": {
+                "stain": stain_out if top3 else "",
+                "label": label_out if labels else "",
+            },
+            "llm_generated_guide": llm_output,
+        }
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    else:
+        print(
+            "⚠️ analysis_type은 'stain_only', 'label_only', 또는 'stain_and_label' 중 하나여야 합니다."
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ai/pipe_final.py b/ai/pipe_final.py
new file mode 100644
index 000000000..ad08b8067
--- /dev/null
+++ b/ai/pipe_final.py
@@ -0,0 +1,512 @@
+#!/usr/bin/env python3
+"""
+MariaDB → YOLO 데이터 복사 → (Re)Train → Optimal per-class conf 찾기 → Evaluate → JSON 저장
+ - Ray-tune 콜백 패치 포함
+ - stain, symbol 모델 모두 fp32→fp16 로드
+ - DB 접속 정보는 실행 시 인자로 전달
+
+"""
+import os
+import sys
+import shutil
+import json
+import time
+import logging
+import types
+import yaml
+import torch
+import numpy as np
+import pymysql
+import argparse
+from PIL import Image, ImageOps
+from collections import defaultdict
+from ultralytics import YOLO
+
+# ──────────────────────────── CLI 인자 파싱 ────────────────────────────
+parser = argparse.ArgumentParser(description="YOLO 파이프라인 실행")
+parser.add_argument("--db-host", required=True, help="DB 호스트")
+parser.add_argument("--db-port", type=int, required=True, help="DB 포트")
+parser.add_argument("--db-user", required=True, help="DB 사용자")
+parser.add_argument("--db-password", required=True, help="DB 비밀번호")
+parser.add_argument("--db-name", required=True, help="DB 이름")
+args = parser.parse_args()
+
+# ──────────────────────────── DB 설정 ────────────────────────────
+DB_HOST = args.db_host
+DB_PORT = args.db_port
+DB_USER = args.db_user
+DB_PASSWORD = args.db_password
+DB_NAME = args.db_name
+SQL_QUERY = (
+    "SELECT stain_image_url, label_image_url "
+    "FROM washing_history "
+    "WHERE estimation = 1 "
+    "  AND (stain_image_url IS NOT NULL OR label_image_url IS NOT NULL);"
+)
+
+# ──────────────────────────── Ray 콜백 패치 (반드시 최상단) ────────────────────────────
+try:
+    import ray
+
+    try:
+        import ray.train._internal.session as _session
+    except ImportError:
+        internal = getattr(ray.train, "_internal", types.ModuleType("_internal"))
+        ray.train._internal = internal
+        session = getattr(internal, "session", types.ModuleType("session"))
+        internal.session = session
+        _session = session
+    if not hasattr(_session, "_get_session"):
+        _session._get_session = lambda: None
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger(__name__).info(
+        "✅ Patched ray.train._internal.session._get_session"
+    )
+except ModuleNotFoundError:
+    pass
+
+# ──────────────────────────── 로깅 설정 ────────────────────────────
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger(__name__)
+
+# ──────────────────────────── 경로 및 설정 ────────────────────────────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+SRC_ROOT = os.path.abspath(os.path.join(BASE_DIR, os.pardir))
+
+STAIN_MODEL_PATH = os.path.join(BASE_DIR, "stain", "stain_cls.pt")
+SYMBOL_MODEL_PATH = os.path.join(BASE_DIR, "symbol", "laundry_labels_cls.pt")
+STAIN_LABEL_DIR = os.path.join(SRC_ROOT, "images", "output", "stain", "labels")
+SYMBOL_LABEL_DIR = os.path.join(SRC_ROOT, "images", "output", "symbol", "labels")
+
+TRAIN_STAIN_IMG_DIR = os.path.join(BASE_DIR, "data", "stain", "train", "images")
+TRAIN_STAIN_LABEL_DIR = os.path.join(BASE_DIR, "data", "stain", "train", "labels")
+TRAIN_SYM_IMG_DIR = os.path.join(BASE_DIR, "data", "symbol", "train", "images")
+TRAIN_SYM_LABEL_DIR = os.path.join(BASE_DIR, "data", "symbol", "train", "labels")
+
+TEST_STAIN_IMG_DIR = os.path.join(BASE_DIR, "data", "stain", "test", "images")
+SYMBOL_TEST_DIR = os.path.join(BASE_DIR, "data", "symbol", "test", "images")
+
+STAIN_DATA_YAML = os.path.join(BASE_DIR, "data", "stain", "data.yaml")
+SYMBOL_DATA_YAML = os.path.join(BASE_DIR, "data", "symbol", "data.yaml")
+
+MODEL_BASE_STAIN = os.path.join(BASE_DIR, "model", "stain")
+MODEL_BASE_SYM = os.path.join(BASE_DIR, "model", "symbol")
+PERF_ROOT = os.path.join(BASE_DIR, os.pardir, "front", "fe-rw", "public", "performance")
+for p in [
+    STAIN_LABEL_DIR,
+    SYMBOL_LABEL_DIR,
+    TRAIN_STAIN_IMG_DIR,
+    TRAIN_STAIN_LABEL_DIR,
+    TRAIN_SYM_IMG_DIR,
+    TRAIN_SYM_LABEL_DIR,
+    TEST_STAIN_IMG_DIR,
+    SYMBOL_TEST_DIR,
+    MODEL_BASE_STAIN,
+    MODEL_BASE_SYM,
+    os.path.join(PERF_ROOT, "stain"),
+    os.path.join(PERF_ROOT, "symbol"),
+]:
+    os.makedirs(p, exist_ok=True)
+
+# ──────────────────────────── 학습 파라미터 ────────────────────────────
+STAIN_CFG = dict(
+    data=STAIN_DATA_YAML,
+    epochs=5,
+    patience=1,
+    batch=2,
+    imgsz=1600,
+    device="cuda:0",
+    workers=1,
+    optimizer="auto",
+    amp=True,
+    mosaic=1.0,
+    mixup=0.0,
+    auto_augment="randaugment",
+    erasing=0.4,
+)
+SYMBOL_CFG = dict(
+    data=SYMBOL_DATA_YAML,
+    epochs=5,
+    patience=1,
+    batch=2,
+    imgsz=2048,
+    device="cuda:0",
+    workers=4,
+    optimizer="SGD",
+    amp=True,
+    cos_lr=True,
+    augment=True,
+    mosaic=True,
+    mixup=0.3,
+)
+
+EVAL_STAIN_SIZE = 320
+CLASS_NAMES_STAIN = [
+    "blood",
+    "coffee",
+    "earth",
+    "ink",
+    "kimchi",
+    "lipstick",
+    "mustard",
+    "oil",
+    "wine",
+]
+CLASS_CONF_THRESH = {
+    "blood": 0.26,
+    "coffee": 0.35,
+    "earth": 0.23,
+    "ink": 0.19,
+    "kimchi": 0.5,
+    "lipstick": 0.33,
+    "mustard": 0.16,
+    "oil": 0.36,
+    "wine": 0.1,
+}
+GLOBAL_CONF = min(CLASS_CONF_THRESH.values())
+
+
+# ──────────────────────────── 유틸 함수 ────────────────────────────
+def get_next_run(root: str) -> str:
+    runs = [int(d) for d in os.listdir(root) if d.isdigit()]
+    return str(max(runs) + 1) if runs else "1"
+
+
+def copy_pair(url: str, dst_img_dir: str, src_lbl_dir: str, dst_lbl_dir: str):
+    """
+    이미지 URL과 대응 라벨(.txt)을 src_lbl_dir에서 찾아
+    dst_img_dir, dst_lbl_dir로 복사
+    """
+    logger.debug(f"DB URL: {url}")
+    if not url:
+        logger.warning("Empty URL, skip.")
+        return
+    # 이미지 경로 해석
+    if os.path.isabs(url) and os.path.exists(url):
+        src_img = url
+        logger.debug(f"Absolute path used: {src_img}")
+    else:
+        rel = url.lstrip("/")
+        src_img = os.path.join(SRC_ROOT, rel)
+        logger.debug(f"Resolved relative to SRC_ROOT: {src_img}")
+    name = os.path.splitext(os.path.basename(src_img))[0]
+    src_lbl = os.path.join(src_lbl_dir, f"{name}.txt")
+    img_exists = os.path.exists(src_img)
+    lbl_exists = os.path.exists(src_lbl)
+    logger.debug(f"Exists? Image: {img_exists}, Label: {lbl_exists}")
+    if img_exists and lbl_exists:
+        shutil.copy2(src_img, os.path.join(dst_img_dir, os.path.basename(src_img)))
+        shutil.copy2(src_lbl, os.path.join(dst_lbl_dir, f"{name}.txt"))
+        logger.info(f"Copied {name} → images and labels dirs")
+    else:
+        if not img_exists:
+            logger.error(f"[MISS-IMG] {src_img} not found")
+        if not lbl_exists:
+            logger.error(f"[MISS-LBL] {src_lbl} not found")
+
+
+# ──────────────────────────── DB에서 이미지 + 라벨 복사 ────────────────────────────
+def db_fetch():
+    logger.info("DB fetch 시작")
+    conn = pymysql.connect(
+        host=DB_HOST,
+        port=DB_PORT,
+        user=DB_USER,
+        password=DB_PASSWORD,
+        database=DB_NAME,
+        charset="utf8mb4",
+    )
+    with conn:
+        cur = conn.cursor()
+        cur.execute(SQL_QUERY)
+        rows = cur.fetchall()
+    for stain_url, label_url in rows:
+        copy_pair(
+            stain_url, TRAIN_STAIN_IMG_DIR, STAIN_LABEL_DIR, TRAIN_STAIN_LABEL_DIR
+        )
+        copy_pair(label_url, TRAIN_SYM_IMG_DIR, SYMBOL_LABEL_DIR, TRAIN_SYM_LABEL_DIR)
+    logger.info("DB fetch 완료")
+
+
+# ──────────────────────────── Ray 콜백 제거 및 모델 로드 ────────────────────────────
+def load_yolo(weights: str, want_gpu: bool = True):
+    if want_gpu and torch.cuda.is_available():
+        try:
+            model = YOLO(weights).to("cuda:0")
+            model.fuse()
+            model = model.to("cuda:0", dtype=torch.float16)
+            logger.info(f"Loaded {weights} on GPU (fp16)")
+            return model, "cuda:0"
+        except RuntimeError as e:
+            if "out of memory" in str(e).lower():
+                logger.warning("GPU OOM → CPU fallback")
+                torch.cuda.empty_cache()
+            else:
+                raise
+    logger.info(f"Loaded {weights} on CPU")
+    return YOLO(weights), "cpu"
+
+
+# ──────────────────────────── per-class conf 튜닝 ────────────────────────────
+def optimize_conf_thresh(weights_path: str):
+    logger.info("Optimizing per-class conf thresholds")
+    model, device = load_yolo(weights_path)
+    records = []  # (gt_idx, pred_idx, pred_conf)
+    resize_pad = lambda im: ImageOps.expand(
+        im,
+        (
+            (max(im.size) - im.size[0]) // 2,
+            (max(im.size) - im.size[1]) // 2,
+            max(im.size) - im.size[0] - (max(im.size) - im.size[0]) // 2,
+            max(im.size) - im.size[1] - (max(im.size) - im.size[1]) // 2,
+        ),
+        fill=(0, 0, 0),
+    ).resize((EVAL_STAIN_SIZE,) * 2)
+
+    # 1) 모든 테스트 이미지에 대해 top1 예측 저장
+    for fn in os.listdir(TEST_STAIN_IMG_DIR):
+        if not fn.lower().endswith((".jpg", ".png")):
+            continue
+        gt = fn.split("_")[0].lower()
+        if gt not in CLASS_NAMES_STAIN:
+            continue
+        gt_idx = CLASS_NAMES_STAIN.index(gt)
+        img = Image.open(os.path.join(TEST_STAIN_IMG_DIR, fn)).convert("RGB")
+        img = resize_pad(img)
+        res = model(img, conf=0.0, device=device)[0]
+        if not res.boxes:
+            records.append((gt_idx, None, 0.0))
+            continue
+        cls = res.boxes.cls.cpu().numpy().astype(int)
+        confs = res.boxes.conf.cpu().numpy()
+        top_idx = confs.argmax()
+        records.append((gt_idx, cls[top_idx], float(confs[top_idx])))
+
+    # 2) 클래스별 F1-opt 임계치 탐색
+    new_thresh = {}
+    for c_idx, c_name in enumerate(CLASS_NAMES_STAIN):
+        total = sum(1 for gt, _, _ in records if gt == c_idx)
+        best_t, best_f1 = 0.0, -1.0
+        for t in np.linspace(0, 1, 101):
+            tp = sum(
+                1
+                for gt, pred, conf in records
+                if gt == c_idx and pred == c_idx and conf >= t
+            )
+            pred_as_c = sum(
+                1 for _, pred, conf in records if pred == c_idx and conf >= t
+            )
+            fp = pred_as_c - tp
+            fn = total - tp
+            P = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+            R = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+            f1 = 2 * P * R / (P + R) if (P + R) > 0 else 0.0
+            if f1 > best_f1:
+                best_f1, best_t = f1, t
+        new_thresh[c_name] = round(best_t, 3)
+        logger.debug(f"Class {c_name}: best_t={best_t:.3f}, F1={best_f1:.4f}")
+
+    new_global = min(new_thresh.values())
+    logger.info(f"Optimized thresholds: {new_thresh}, GLOBAL_CONF={new_global:.3f}")
+    return new_thresh, new_global
+
+
+# ──────────────────────────── 평가: Stain (miss 제외) ────────────────
+def evaluate_stain(weights_path: str):
+    logger.info(f"Evaluating stain: {weights_path}")
+    model, device = load_yolo(weights_path)
+
+    def resize_pad(im):
+        w, h = im.size
+        m = max(w, h)
+        pad = ((m - w) // 2, (m - h) // 2, m - w - (m - w) // 2, m - h - (m - h) // 2)
+        return ImageOps.expand(im, pad, fill=(0, 0, 0)).resize((EVAL_STAIN_SIZE,) * 2)
+
+    stats = {
+        "s": defaultdict(int),
+        "m": defaultdict(int),
+        "t1": defaultdict(int),
+        "t3": defaultdict(int),
+    }
+    inf_t = 0.0
+    for fn in os.listdir(TEST_STAIN_IMG_DIR):
+        if not fn.lower().endswith((".jpg", ".png")):
+            continue
+        gt = fn.split("_")[0].lower()
+        if gt not in CLASS_NAMES_STAIN:
+            continue
+        idx = CLASS_NAMES_STAIN.index(gt)
+        stats["s"][idx] += 1
+        img = resize_pad(
+            Image.open(os.path.join(TEST_STAIN_IMG_DIR, fn)).convert("RGB")
+        )
+        t0 = time.time()
+        res = model(img, conf=GLOBAL_CONF, device=device)[0]
+        inf_t += time.time() - t0
+        if not res.boxes:
+            stats["m"][idx] += 1
+            continue
+        cls = res.boxes.cls.cpu().numpy().astype(int)
+        confs = res.boxes.conf.cpu().numpy()
+        keep = np.array(
+            [
+                confs[i] >= CLASS_CONF_THRESH[CLASS_NAMES_STAIN[c]]
+                for i, c in enumerate(cls)
+            ],
+            dtype=bool,
+        )
+        cls, confs = cls[keep], confs[keep]
+        if cls.size == 0:
+            stats["m"][idx] += 1
+            continue
+        order = confs.argsort()[::-1]
+        top3 = cls[order[:3]]
+        if idx == top3[0]:
+            stats["t1"][idx] += 1
+        if idx in top3:
+            stats["t3"][idx] += 1
+
+    per, tot_s, tot_m, tot1, tot3 = {}, 0, 0, 0, 0
+    for i, name in enumerate(CLASS_NAMES_STAIN):
+        s = stats["s"][i]
+        m = stats["m"][i]
+        non_miss = s - m
+        o1 = stats["t1"][i]
+        o3 = stats["t3"][i]
+        if non_miss > 0:
+            per[name] = {
+                "samples": s,
+                "miss": m,
+                "top1_acc": round(o1 / non_miss, 4),
+                "top3_acc": round(o3 / non_miss, 4),
+            }
+            tot_s += s
+            tot_m += m
+            tot1 += o1
+            tot3 += o3
+    overall = {}
+    total_non_miss = tot_s - tot_m
+    if total_non_miss > 0:
+        overall = {
+            "samples": tot_s,
+            "miss": tot_m,
+            "top1_acc": round(tot1 / total_non_miss, 4),
+            "top3_acc": round(tot3 / total_non_miss, 4),
+            "precision": round(tot1 / total_non_miss, 4),
+            "recall": round(tot1 / total_non_miss, 4),
+            "inference_time": {
+                "total_s": round(inf_t, 4),
+                "avg_per_image_s": round(inf_t / tot_s, 4),
+            },
+        }
+    logger.info(f"Stain overall (miss excluded): {overall}")
+    return {"per_class": per, "overall": overall}
+
+
+# ──────────────────────────── 평가: Symbol (Zero-AP 제외 + CONF_THRESH/AUGMENT 적용) ────────────────────────────
+SYMBOL_CONF = 0.1
+SYMBOL_AUG = True
+
+
+def evaluate_symbol(weights_path: str):
+    logger.info(f"Evaluating symbol: {weights_path}")
+    model, device = load_yolo(weights_path)
+    first = model.val(
+        data=SYMBOL_DATA_YAML,
+        split="test",
+        imgsz=SYMBOL_CFG["imgsz"],
+        conf=SYMBOL_CONF,
+        augment=False,
+        device=device,
+        verbose=False,
+    )
+    initial_maps = dict(zip(first.ap_class_index, first.maps))
+    valid_ids = [int(idx) for idx, ap in initial_maps.items() if ap > 0.0]
+    logger.debug(f"Symbol valid IDs (AP>0): {valid_ids}")
+    final = model.val(
+        data=SYMBOL_DATA_YAML,
+        split="test",
+        imgsz=SYMBOL_CFG["imgsz"],
+        conf=SYMBOL_CONF,
+        augment=SYMBOL_AUG,
+        classes=valid_ids,
+        device=device,
+        verbose=False,
+    )
+    P, R, mAP50, mAP5095 = final.box.mean_results()
+    inf_ms = final.speed.get("inference", 0.0)
+    names = yaml.safe_load(open(SYMBOL_DATA_YAML))["names"]
+    ap_map = {int(idx): float(ap) for idx, ap in zip(final.ap_class_index, final.maps)}
+    per_cls = {name: ap_map.get(i, 0.0) for i, name in enumerate(names)}
+    metrics = {
+        "precision": P,
+        "recall": R,
+        "mAP50": mAP50,
+        "mAP50-95": mAP5095,
+        "inference_time_ms": inf_ms,
+        "per_class": per_cls,
+    }
+    logger.info(f"Symbol metrics (filtered): {metrics}")
+    return metrics
+
+
+# ──────────────────────────── 학습 & 평가 파이프라인 ────────────────────────────
+def retrain_and_eval():
+    logger.info("Starting retrain & eval pipeline")
+    # Stain 모델 재학습 + conf 튜닝 + 평가
+    if os.listdir(TRAIN_STAIN_IMG_DIR):
+        run = get_next_run(MODEL_BASE_STAIN)
+        model = YOLO(STAIN_MODEL_PATH)
+        model.callbacks = []
+        model.train(project=MODEL_BASE_STAIN, name=run, save=True, **STAIN_CFG)
+        best = os.path.join(MODEL_BASE_STAIN, run, "weights", "best.pt")
+        logger.info(f"[Stain] Trained -> {best}")
+        new_thresh, new_global = optimize_conf_thresh(best)
+        CLASS_CONF_THRESH.clear()
+        CLASS_CONF_THRESH.update(new_thresh)
+        global GLOBAL_CONF
+        GLOBAL_CONF = new_global
+        res = evaluate_stain(best)
+        out = {
+            "model_version": float(run) / 10,
+            "model_type": "stain",
+            "weights_path": best,
+            "metrics": res,
+        }
+        p = os.path.join(PERF_ROOT, "stain", "performance.json")
+        json.dump(out, open(p, "w", encoding="utf8"), ensure_ascii=False, indent=2)
+        logger.info(f"[Stain] Report -> {p}")
+    else:
+        logger.error("No stain train images found; skipping stain")
+    # Symbol 모델 재학습 + 평가
+    if os.listdir(TRAIN_SYM_IMG_DIR):
+        run = get_next_run(MODEL_BASE_SYM)
+        model = YOLO(SYMBOL_MODEL_PATH)
+        model.callbacks = []
+        model.train(project=MODEL_BASE_SYM, name=run, save=True, **SYMBOL_CFG)
+        best = os.path.join(MODEL_BASE_SYM, run, "weights", "best.pt")
+        logger.info(f"[Symbol] Trained -> {best}")
+        res = evaluate_symbol(best)
+        out = {
+            "model_version": float(run) / 10,
+            "model_type": "symbol",
+            "weights_path": best,
+            "metrics": res,
+        }
+        p = os.path.join(PERF_ROOT, "symbol", "performance.json")
+        json.dump(out, open(p, "w", encoding="utf8"), ensure_ascii=False, indent=2)
+        logger.info(f"[Symbol] Report -> {p}")
+    else:
+        logger.error("No symbol train images found; skipping symbol")
+    logger.info("Retrain & eval pipeline complete")
+
+
+if __name__ == "__main__":
+    logger.info("── PIPELINE START ──")
+    db_fetch()
+    retrain_and_eval()
+    logger.info("── PIPELINE COMPLETE ──")
diff --git a/ai/prompt.py b/ai/prompt.py
new file mode 100644
index 000000000..958891096
--- /dev/null
+++ b/ai/prompt.py
@@ -0,0 +1,86 @@
+# test_llm_only.py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import os
+
+# ───── 모델 로딩 ─────
+LLM_MODEL_DIR = "llm/kanana-nano-2.1b-base"
+
+tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_DIR, padding_side="left")
+tokenizer.pad_token = tokenizer.eos_token
+try:
+    model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_DIR, torch_dtype=torch.bfloat16, trust_remote_code=True
+    ).to("cuda")
+except (RuntimeError, ValueError):
+    print("⚠️ bfloat16 미지원 → float32로 재시도")
+    model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_DIR, torch_dtype=torch.float32, trust_remote_code=True
+    ).to("cuda")
+
+
+# ───── 프롬프트 생성 함수 ─────
+def build_llm_prompt(stain_class, stain_advices, label_expls):
+    stain_list_text = "\n".join(
+        [f"{i+1}. {advice}" for i, advice in enumerate(stain_advices)]
+    )
+    label_text = (
+        "세탁 기호가 감지되지 않았습니다."
+        if not label_expls
+        else ", ".join(label_expls)
+    )
+
+    return (
+        f"얼룩 종류: {stain_class}\n"
+        f"감지된 세탁 기호: {label_text}\n"
+        f"가능한 얼룩 제거법:\n{stain_list_text}\n\n"
+        f"조건:\n"
+        f"- 금지된 세탁 조건을 절대 위반하지 마세요.\n"
+        f"- 제거법 중 세탁 기호에 위배되지 않는 하나만 선택하세요.\n"
+        f"- 선택한 제거법을 명령형 한 문장으로 작성하세요.\n"
+        f"- 다른 문장 없이 그 문장 하나만 출력하세요.\n"
+        f"- 세탁이 불가능하다면 '물세탁이 불가능하므로 세탁소에 맡기세요.'라고 출력하세요.\n\n"
+        f"세탁 방법:"
+    )
+
+
+# ───── 테스트 입력 ─────
+stain_class = "blood"
+stain_advices = [
+    "찬물로 씻은 후 과산화수소를 뿌려 살살 비벼준 다음 다시 찬물로 헹구어 세탁한다.",
+    "따뜻한 물과 비누로 얼룩 부위를 살짝 문지른 뒤, 소금물에 담가 거품을 내며 세탁한다.",
+    "피가 굳은 경우, 치약을 발라 10분간 둔 뒤 굳은 부분을 찬물로 헹구어 제거한다.",
+]
+label_expls = [
+    "회전식 건조기 사용 금지.",
+    "세탁 금지: 물세탁을 하지 마세요.",
+    "표백 금지: 산소계, 염소계 표백제를 모두 사용하지 마세요.",
+    "다림질 금지: 열에 의해 손상될 수 있습니다.",
+    "드라이클리닝 가능.",
+]
+label_warnings = [desc for desc in label_expls if "금지" in desc]
+
+# ───── LLM 실행 ─────
+prompt = build_llm_prompt(stain_class, stain_advices, label_expls)
+
+input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")["input_ids"]
+with torch.no_grad():
+    output = model.generate(
+        input_ids,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.85,
+        top_k=30,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+
+if "세탁 방법:" in decoded:
+    result = decoded.split("세탁 방법:")[-1].strip()
+else:
+    result = decoded.strip()
+
+print("───────────────────── LLM 결과 ─────────────────────")
+print(result)
+print("─────────────────────────────────────────────────────")
diff --git a/ai/stain/stain_cls.pt b/ai/stain/stain_cls.pt
new file mode 100644
index 000000000..1d6c01291
Binary files /dev/null and b/ai/stain/stain_cls.pt differ
diff --git a/ai/stain/stain_washing_guidelines.json b/ai/stain/stain_washing_guidelines.json
new file mode 100644
index 000000000..6444e6206
--- /dev/null
+++ b/ai/stain/stain_washing_guidelines.json
@@ -0,0 +1,52 @@
+{
+  "blood": [
+    "찬물로 씻은 후 과산화수소를 뿌려 살살 비벼준 다음 다시 찬물로 헹구어 세탁한다.",
+    "따뜻한 물과 비누로 얼룩 부위를 살짝 문지른 뒤, 소금물에 담가 거품을 내며 세탁한다.",
+    "피가 굳은 경우, 치약을 발라 10분간 둔 뒤 굳은 부분을 찬물로 헹구어 제거한다."
+  ],
+  "coffee": [
+    "소금을 미지근한 물에 녹여 얼룩 부위에 묻혀 가볍게 문질러 세탁한다.",
+    "무당·무색소 탄산수를 얼룩에 적신 뒤 티슈로 두드려 흡수시켜 세탁한다.",
+    "글리세린을 얼룩에 바른 후 20~30분 방치한 뒤 찬물로 헹구어 세탁한다."
+  ],
+  "earth": [
+    "알코올을 얼룩에 바른 후 비누로 손세탁하여 제거한다.",
+    "마른 흙을 털어낸 후 식초와 주방세제를 1:1로 섞어 문지르고 과탄산소다에 담가 세탁한다.",
+    "얼룩 부위를 찬물로 헹군 뒤 세제를 직접 바르고 10분 후 문질러 세탁한다.",
+    "과탄산소다와 주방세제를 푼 따뜻한 물에 1시간 담근 뒤 솔로 문질러 세탁한다.",
+    "식빵을 얼룩 부위에 문질러 흡착시켜 제거한다.",
+    "감자를 문질러 전분 성분을 입힌 후 주방세제를 바르고 문질러 세탁한다.",
+    "구연산 발포제를 뿌린 뒤 얼룩을 닦아내어 제거한다."
+  ],
+  "ink": [
+    "알콜솜을 얼룩 부위에 대고 두드려가며 잉크를 흡수시켜 제거한다.",
+    "헤어스프레이를 뿌린 뒤 수건으로 톡톡 두드려 흡수시켜 제거한다.",
+    "차가운 우유를 뿌려 30분간 방치한 후 기존 세제로 세탁하여 제거한다.",
+    "아세톤을 천에 묻혀 얼룩 부위를 꾹꾹 눌러 흡수시켜 제거한다."
+  ],
+  "kimchi": [
+    "베이킹소다와 세제를 혼합해 문질러 미지근한 물로 세탁한다.",
+    "주방세제와 식초를 혼합해 얼룩 부위를 문질러 미지근한 물로 세탁한다.",
+    "세탁 후 잔여 얼룩이 있다면 햇볕에 말려 자연 탈색시켜 제거한다."
+  ],
+  "lipstick": [
+    "알코올을 탈지면에 묻혀 얼룩 부위를 문질러 제거한다.",
+    "클렌징 티슈로 얼룩 부위를 반복적으로 문질러 제거한다.",
+    "헤어스프레이를 뿌려 말린 뒤 물티슈로 닦아내어 제거한다."
+  ],
+  "mustard": [
+    "찬물에 적신 뒤 중성세제로 문지르고 산소계 표백제에 담가 세탁한다.",
+    "과탄산 소다 또는 베이킹 소다를 얼룩에 바른 후 물로 헹구어 세탁한다.",
+    "식초, 주방세제, 물을 1:1:2로 혼합해 바르고 10분 후 헹구어 세탁한다."
+  ],
+  "oil": [
+    "식초를 얼룩 부위에 바르고 가볍게 문질러 바로 세탁한다.",
+    "레몬즙을 수건에 적셔 얼룩을 두드린 뒤 5~10분 후 세탁한다.",
+    "색 옷을 콜라에 담갔다가 얼룩 부위를 문질러 세탁한다."
+  ],
+  "wine": [
+    "젖은 티슈로 얼룩을 찍어낸 후 주방세제로 문질러 60°C 물로 세탁한다.",
+    "산소계 표백제에 10분간 담갔다가 손세탁 또는 세탁기로 세탁한다.",
+    "따뜻한 소금물로 얼룩 부위를 문지르고 탄산수로 헹구어 세탁한다."
+  ]
+}
diff --git a/ai/symbol/label_symbol_guide.json b/ai/symbol/label_symbol_guide.json
new file mode 100644
index 000000000..9553b0a15
--- /dev/null
+++ b/ai/symbol/label_symbol_guide.json
@@ -0,0 +1,45 @@
+{
+  "30C": "30도에서 약하게 세탁하세요. 울이나 섬세한 의류에 적합합니다.",
+  "40C": "40도 이하에서 일반 세탁 가능합니다.",
+  "50C": "50도 이하에서 세탁 가능합니다. 약간 강한 세탁이 가능합니다.",
+  "60C": "60도 이하에서 세탁 가능합니다. 속옷이나 침구류 등 위생 세탁에 적합합니다.",
+  "70C": "70도 이하 고온 세탁 가능. 세균 제거 목적에 적합합니다.",
+  "95C": "95도 고온 세탁 가능. 삶는 세탁도 가능합니다.",
+  "DN_bleach": "표백 금지: 산소계, 염소계 표백제를 모두 사용하지 마세요.",
+  "DN_dry": "건조 금지: 열 건조나 자연 건조 모두 피해야 합니다.",
+  "DN_dry_clean": "드라이클리닝 금지: 전문가 세탁소에 맡기는 것도 금지입니다.",
+  "DN_iron": "다림질 금지: 열에 의해 손상될 수 있습니다.",
+  "DN_steam": "스팀 금지: 스팀 다림질은 하지 마세요.",
+  "DN_tumble_dry": "회전식 건조기 사용 금지.",
+  "DN_wash": "세탁 금지: 물세탁을 하지 마세요.",
+  "DN_wet_clean": "습식 세탁 금지: 전문가용 습식 세탁도 금지입니다.",
+  "DN_wring": "짜지 마세요: 탈수나 손으로 비틀어 짜는 행위는 금지입니다.",
+  "bleach": "표백 가능: 필요 시 산소계 또는 염소계 표백제 사용 가능합니다.",
+  "chlorine_bleach": "염소계 표백제 사용 가능.",
+  "non_chlorine_bleach": "염소계 표백제 금지, 산소계 표백제만 허용됩니다.",
+  "drip_dry": "물기가 뚝뚝 떨어지게 널어 말리세요. 탈수하지 마세요.",
+  "drip_dry_in_shade": "그늘에서 뚝뚝 떨어지게 말리세요.",
+  "dry_flat": "편평하게 눕혀서 건조하세요. 옷의 변형 방지를 위해 적합합니다.",
+  "dry_flat_in_shade": "그늘에 눕혀서 건조하세요.",
+  "line_dry": "옷걸이나 줄에 널어 말리세요.",
+  "line_dry_in_shade": "그늘에서 옷걸이나 줄에 널어 말리세요.",
+  "natural_dry": "자연 건조 가능: 일반적인 실내 혹은 야외 건조.",
+  "shade_dry": "직사광선을 피하고 그늘에서 말리세요.",
+  "dry_clean": "드라이클리닝 가능.",
+  "dry_clean_any_solvent_except_trichloroethylene": "트리클로로에틸렌 제외 모든 용제로 드라이클리닝 가능.",
+  "dry_clean_petrol_only": "석유계 용제만 사용한 드라이클리닝 가능.",
+  "hand_wash": "손세탁 권장: 약한 세제와 미온수 사용.",
+  "machine_wash": "세탁기 사용 가능.",
+  "iron": "다림질 가능. 온도에 유의하세요.",
+  "iron_high": "고온 다림질 가능: 약 200°C 이하에서 사용.",
+  "iron_medium": "중온 다림질 가능: 약 150°C 이하 권장.",
+  "iron_low": "저온 다림질 가능: 약 110°C 이하 권장.",
+  "steam": "스팀 다림질 가능.",
+  "tumble_dry_normal": "회전식 건조기 사용 가능 (보통 온도).",
+  "tumble_dry_low": "회전식 건조기 저온 사용 권장.",
+  "tumble_dry_medium": "회전식 건조기 중온 사용 가능.",
+  "tumble_dry_high": "회전식 건조기 고온 사용 가능.",
+  "tumble_dry_no_heat": "열 없이 회전 건조만 가능합니다.",
+  "wet_clean": "습식 세탁 가능: 전문가용 습식 세탁 허용.",
+  "wring": "비틀어 짜기 가능."
+}
diff --git a/ai/symbol/laundry_labels_cls.pt b/ai/symbol/laundry_labels_cls.pt
new file mode 100644
index 000000000..35605439b
Binary files /dev/null and b/ai/symbol/laundry_labels_cls.pt differ
diff --git a/ai/t_main.py b/ai/t_main.py
new file mode 100644
index 000000000..e01f0833a
--- /dev/null
+++ b/ai/t_main.py
@@ -0,0 +1,377 @@
+import os
+import json
+import cv2
+import numpy as np
+from pathlib import Path
+from ultralytics import YOLO
+import torch
+import sys
+import random
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ───── 경로 설정 ─────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+STAIN_MODEL_PATH = os.path.join(BASE_DIR, "stain", "stain_cls.pt")
+LABEL_MODEL_PATH = os.path.join(BASE_DIR, "symbol", "laundry_labels_cls.pt")
+STAIN_GUIDE_PATH = os.path.join(BASE_DIR, "stain", "stain_washing_guidelines.json")
+LABEL_GUIDE_PATH = os.path.join(BASE_DIR, "symbol", "label_symbol_guide.json")
+OUT_DIR = os.path.abspath(os.path.join(BASE_DIR, "..", "images"))
+
+# 로컬 LLM 모델 경로 설정 (사전에 다운로드된 모델 디렉토리)
+LLM_MODEL_DIR = os.path.join(BASE_DIR, "llm/kanana-nano-2.1b-base")
+# LLM 모델 로딩
+llm_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_DIR, padding_side="left")
+llm_tokenizer.pad_token = llm_tokenizer.eos_token
+
+try:
+    llm_model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_DIR, torch_dtype=torch.bfloat16, trust_remote_code=True
+    ).to("cuda")
+except (RuntimeError, ValueError) as e:
+    print("⚠️ bfloat16 미지원 → float32로 재시도")
+    llm_model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_DIR, torch_dtype=torch.float32, trust_remote_code=True
+    ).to("cuda")
+
+
+# ───── 클래스 및 설정 ─────
+STAIN_CLASSES = [
+    "blood",
+    "coffee",
+    "earth",
+    "ink",
+    "kimchi",
+    "lipstick",
+    "mustard",
+    "oil",
+    "wine",
+]
+CLASS_CONF_TH = {
+    "blood": 0.260,
+    "coffee": 0.350,
+    "earth": 0.230,
+    "ink": 0.190,
+    "kimchi": 0.500,
+    "lipstick": 0.330,
+    "mustard": 0.160,
+    "oil": 0.360,
+    "wine": 0.100,
+}
+GLOBAL_CONF = min(CLASS_CONF_TH.values())
+LABEL_CONF = 0.5
+TOP_K = 3
+
+
+def build_llm_prompt(stain_class, stain_advices, label_expls):
+    if not stain_class or not stain_advices:
+        return ""
+
+    # 얼룩 제거법 후보 정리
+    stain_list_text = "\n".join(
+        [f"{i+1}. {advice}" for i, advice in enumerate(stain_advices)]
+    )
+
+    # 세탁 기호 요약 설명 정리
+    label_text = (
+        "세탁 조건이 감지되지 않았습니다."
+        if not label_expls
+        else "\n" + "\n".join(f"- {e}" for e in label_expls)
+    )
+
+    # 프롬프트 템플릿
+    prompt = f"""당신은 얼룩 제거 전문가입니다.
+
+다음은 옷의 얼룩 종류와 세탁 조건에 대한 정보입니다:
+
+▸ 얼룩 종류: {stain_class}
+▸ 얼룩 제거법 후보:
+{stain_list_text}
+▸ 옷의 세탁 조건:
+{label_text}
+
+이 정보를 참고하여 얼룩 제거 방법을 아래 조건에 맞게 안내해 주세요:
+
+- **가장 적절한 제거 방법 하나만 선택**하여 설명하세요. 나머지는 언급하지 마세요.
+- '세탁 기호'나 '기호명' 같은 표현은 사용하지 마세요.
+- 세탁 조건은 자연스럽게 문장 안에 녹여 설명하세요.  
+  예: '표백 금지' → '표백제는 사용하지 마세요'  
+      '회전식 건조 금지' → '자연 건조를 권장합니다'
+- 인사말 없이, 친절하고 명확한 한 문단으로 작성하세요.
+
+세탁 방법:"""
+    return prompt
+
+
+# ───── 모델 및 가이드 로딩 ─────
+stain_model = YOLO(STAIN_MODEL_PATH)
+label_model = YOLO(LABEL_MODEL_PATH)
+with open(STAIN_GUIDE_PATH, "r", encoding="utf-8") as f:
+    stain_guide = json.load(f)
+with open(LABEL_GUIDE_PATH, "r", encoding="utf-8") as f:
+    label_guide = json.load(f)
+
+
+# ───── stain 예측 ─────
+def predict_stain(image_path):
+    result = stain_model(image_path, conf=GLOBAL_CONF, imgsz=320)[0]
+    if result.boxes is None or len(result.boxes) == 0:
+        return None, None
+
+    classes = result.boxes.cls.cpu().numpy().astype(int)
+    probs = result.boxes.conf.cpu().numpy()
+    boxes = result.boxes.xyxy.cpu().numpy()
+
+    keep = np.array(
+        [
+            probs[i] >= CLASS_CONF_TH[STAIN_CLASSES[cls]]
+            for i, cls in enumerate(classes)
+        ],
+        dtype=bool,
+    )
+
+    if not keep.any():
+        return None, None
+    classes, probs, boxes = classes[keep], probs[keep], boxes[keep]
+    if len(classes) == 0:
+        return [], ""
+
+    sorted_idx = probs.argsort()[::-1]
+    top3 = [
+        (STAIN_CLASSES[classes[i]], round(float(probs[i]), 3))
+        for i in sorted_idx[:TOP_K]
+    ]
+    top1_idx = sorted_idx[0]
+
+    # 시각화
+    img = cv2.imread(image_path)
+    h, w = img.shape[:2]
+    for i in range(len(classes)):
+        x1, y1, x2, y2 = map(int, boxes[i])
+        cls_id = classes[i]
+        label = f"{STAIN_CLASSES[cls_id]} {probs[i]:.2f}"
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
+        cv2.putText(
+            img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2
+        )
+
+    name = Path(image_path).stem
+    out_dir = os.path.join(OUT_DIR, "output", "stain")
+    os.makedirs(os.path.join(out_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(out_dir, "labels"), exist_ok=True)
+
+    image_out = os.path.join(out_dir, "images", f"{name}_stain.jpg")
+    label_out = os.path.join(out_dir, "labels", f"{name}.txt")
+
+    # Top-1 라벨 저장
+    with open(label_out, "w") as f:
+        i = top1_idx
+        x1, y1, x2, y2 = map(int, boxes[i])
+        xc = ((x1 + x2) / 2) / w
+        yc = ((y1 + y2) / 2) / h
+        bw = (x2 - x1) / w
+        bh = (y2 - y1) / h
+        f.write(f"{classes[i]} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}\n")
+
+    cv2.imwrite(image_out, img)
+    return top3, os.path.relpath(image_out, os.path.dirname(OUT_DIR)).replace("\\", "/")
+
+
+# ───── symbol 예측 ─────
+def predict_label(image_path):
+    result = label_model.predict(
+        source=image_path,
+        imgsz=2048,
+        conf=LABEL_CONF,
+        iou=0.35,
+        max_det=1000,
+        agnostic_nms=True,
+        augment=True,
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        verbose=False,
+    )[0]
+
+    if result.boxes is None or len(result.boxes) == 0:
+        return None, None
+    boxes = result.boxes.xyxy.cpu().numpy()
+    classes = result.boxes.cls.cpu().numpy().astype(int)
+    probs = result.boxes.conf.cpu().numpy()
+
+    img = cv2.imread(image_path)
+    h, w = img.shape[:2]
+    name = Path(image_path).stem
+
+    out_dir = os.path.join(OUT_DIR, "output", "symbol")
+    os.makedirs(os.path.join(out_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(out_dir, "labels"), exist_ok=True)
+
+    image_out = os.path.join(out_dir, "images", f"{name}_symbol.jpg")
+    label_out = os.path.join(out_dir, "labels", f"{name}.txt")
+
+    with open(label_out, "w") as f:
+        for cls_id, conf, box in zip(classes, probs, boxes):
+            x1, y1, x2, y2 = map(int, box)
+            class_name = label_model.names[cls_id]
+            label = f"{class_name} {conf:.2f}"
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(
+                img, label, (x1, y1 - 20), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), 4
+            )
+
+            xc = ((x1 + x2) / 2) / w
+            yc = ((y1 + y2) / 2) / h
+            bw = (x2 - x1) / w
+            bh = (y2 - y1) / h
+            f.write(f"{cls_id} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}\n")
+
+    cv2.imwrite(image_out, img)
+    label_names = list(set(label_model.names[cls] for cls in classes))
+    return label_names, os.path.relpath(image_out, os.path.dirname(OUT_DIR)).replace(
+        "\\", "/"
+    )
+
+
+# ───── 실행 진입점 ─────
+def main():
+    if len(sys.argv) < 3:
+        print("사용법: python laundry_pipeline.py <analysis_type> <image_path>")
+        sys.exit(1)
+
+    analysis_type = sys.argv[1]
+    image_path = sys.argv[2]
+
+    if analysis_type == "stain_only":
+        top3, output_path = predict_stain(image_path)
+        if top3 is None:
+            output = {
+                "detected_stain": {
+                    "top3": [{"class": "", "confidence": ""} for _ in range(3)]
+                },
+                "washing_instructions": [{"class": "", "instructions": ["", "", ""]}],
+                "output_image_path": "",
+            }
+        else:
+            output = {
+                "detected_stain": {
+                    "top3": [{"class": c, "confidence": s} for c, s in top3]
+                },
+                "washing_instructions": [],
+                "output_image_path": output_path,
+            }
+
+            seen_classes = set()
+
+            for stain, _ in top3:
+                if stain in seen_classes:
+                    continue  # 중복 방지
+                seen_classes.add(stain)
+
+                methods = stain_guide.get(stain, [])
+                if isinstance(methods, list) and len(methods) > 0:
+                    first = methods[0]
+                    remaining = methods[1:]
+                    rand = random.sample(remaining, k=min(2, len(remaining)))
+                    combined = [first] + rand
+                else:
+                    combined = ["정보 없음"]
+
+                output["washing_instructions"].append(
+                    {"class": stain, "instructions": combined}
+                )
+
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    elif analysis_type == "label_only":
+        labels, output_path = predict_label(image_path)
+        if labels is None:
+            output = {
+                "detected_labels": [],
+                "label_explanation": [],
+                "output_image_path": "",
+            }
+        else:
+            output = {
+                "detected_labels": labels,
+                "label_explanation": [
+                    label_guide.get(lbl, "정보 없음") for lbl in labels
+                ],
+                "output_image_path": output_path,
+            }
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    elif analysis_type == "stain_and_label":
+        if len(sys.argv) < 4:
+            print(
+                "⚠️ stain_and_label 분석에는 이미지 2장(stain, label) 경로가 필요합니다."
+            )
+            sys.exit(1)
+
+        stain_img = sys.argv[2]
+        label_img = sys.argv[3]
+
+        top3, stain_out = predict_stain(stain_img)
+        labels, label_out = predict_label(label_img)
+
+        if top3 is None:
+            stain_class = ""
+            stain_advices = []
+        else:
+            stain_class = top3[0][0]
+            all_methods = stain_guide.get(stain_class, [])
+            if isinstance(all_methods, list) and len(all_methods) > 0:
+                first = all_methods[0]
+                remaining = all_methods[1:]
+                rand = random.sample(remaining, k=min(2, len(remaining)))
+                stain_advices = [first] + rand
+            else:
+                stain_advices = ["정보 없음"]
+
+        label_expls = [label_guide.get(lbl, "") for lbl in labels] if labels else []
+
+        # DN_wash가 포함되어 있다면 고정된 멘트 출력
+        if any("세탁 금지" in expl for expl in label_expls):
+            llm_output = "감지된 세탁 기호에 따라 물세탁이 불가하여 가정에서 얼룩 제거가 어려운 제품입니다. 반드시 전문 세탁소에 의뢰하시기 바랍니다."
+        elif not stain_class:
+            llm_output = ""
+        else:
+            prompt = build_llm_prompt(stain_class, stain_advices, label_expls)
+            input_ids = llm_tokenizer(prompt, return_tensors="pt").to("cuda")[
+                "input_ids"
+            ]
+            with torch.no_grad():
+                output = llm_model.generate(
+                    input_ids,
+                    max_new_tokens=256,
+                    do_sample=True,
+                    temperature=0.6,
+                    top_p=0.85,
+                    top_k=30,
+                    pad_token_id=llm_tokenizer.eos_token_id,
+                )
+            decoded = llm_tokenizer.decode(output[0], skip_special_tokens=True)
+            llm_output = (
+                decoded.split("세탁 방법:")[-1].strip()
+                if "세탁 방법:" in decoded
+                else decoded.strip()
+            )
+
+        output = {
+            "top1_stain": stain_class,
+            "washing_instructions": stain_advices,
+            "detected_labels": labels if labels else [],
+            "label_explanation": label_expls,
+            "output_image_paths": {
+                "stain": stain_out if top3 else "",
+                "label": label_out if labels else "",
+            },
+            "llm_generated_guide": llm_output,
+        }
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    else:
+        print(
+            "⚠️ analysis_type은 'stain_only', 'label_only', 또는 'stain_and_label' 중 하나여야 합니다."
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ai/test.py b/ai/test.py
new file mode 100644
index 000000000..d175f0361
--- /dev/null
+++ b/ai/test.py
@@ -0,0 +1,15 @@
+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "kakaocorp/kanana-nano-2.1b-base"
+save_dir = "./llm/kanana-nano-2.1b-base"
+
+# 1. 저장 경로 디렉토리 생성 (존재하지 않으면 생성)
+os.makedirs(save_dir, exist_ok=True)
+
+# 2. 모델과 토크나이저 다운로드 및 저장
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+tokenizer.save_pretrained(save_dir)
+model.save_pretrained(save_dir)
diff --git a/ai/train/train_stain_model.py b/ai/train/train_stain_model.py
new file mode 100644
index 000000000..a562779d2
--- /dev/null
+++ b/ai/train/train_stain_model.py
@@ -0,0 +1,72 @@
+import os
+import torch
+import random
+import numpy as np
+from ultralytics import YOLO
+
+# ───── 경로 설정 ─────
+DATA_YAML = "/content/drive/MyDrive/dataset_stain/data.yaml"
+PROJECT_DIR = "/content/drive/MyDrive/yolov8m_stain"
+PRETRAINED = os.path.join(PROJECT_DIR, "stain_yolov8s_1600_a1", "weights", "last.pt")
+
+# ───── 재현성용 시드 고정 ─────
+seed = 0
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+
+# ───── 모델 로드 (이전 체크포인트) ─────
+model = YOLO(PRETRAINED)
+
+# ───── 학습 파라미터 ─────
+train_params = dict(
+    data=DATA_YAML,
+    epochs=500,  # 전체 에폭 수 (이어서: 이전 훈련 포함)
+    patience=15,  # 조기종료 patience
+    batch=8,
+    imgsz=1600,
+    save=True,
+    save_period=-1,
+    cache=False,
+    device="cuda:0",
+    workers=1,
+    project=PROJECT_DIR,
+    name="stain_yolov8s_1600_a1",
+    exist_ok=False,
+    pretrained=True,
+    optimizer="auto",
+    verbose=True,
+    seed=seed,
+    deterministic=True,
+    single_cls=False,
+    rect=False,
+    cos_lr=False,
+    close_mosaic=10,
+    resume=True,  # ← 이전 훈련 이어서
+    amp=True,
+    fraction=1.0,
+    profile=False,
+    freeze=None,
+    multi_scale=False,
+    overlap_mask=True,
+    mask_ratio=4,
+    dropout=0.0,
+    val=True,
+    split="val",
+    save_json=False,
+    save_hybrid=False,
+    conf=None,
+    iou=0.7,
+    max_det=300,
+    half=False,
+    dnn=False,
+    plots=True,
+    augment=False,
+    agnostic_nms=False,
+    classes=None,
+    retina_masks=False,
+    show=False,
+    save_frames=False,
+    save_txt=False,
+    save_conf=False,
+)
diff --git a/ai/train/train_symbol_model.py b/ai/train/train_symbol_model.py
new file mode 100644
index 000000000..efcf61274
--- /dev/null
+++ b/ai/train/train_symbol_model.py
@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+import os
+# (선택) 메모리 단편화 방지
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+# ─── Ray 2.x 세션 이름 바인딩 패치 ─────────────────────────
+try:
+    import ray.train._internal.session as _internal_session
+    if hasattr(_internal_session, 'get_session') and not hasattr(_internal_session, '_get_session'):
+        _internal_session._get_session = _internal_session.get_session
+    print("[Patch] Ray 2.x 호환성 패치 완료")
+except Exception as e:
+    print(f"[Warning] Ray 패치 실패 ({e}), RayTuneCallback 에러가 발생할 수 있음")
+# ───────────────────────────────────────────────────────────
+
+import torch
+from ultralytics import YOLO
+
+
+def main():
+    # 디바이스 설정
+    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+    print(f"[Train] Using device: {device}")
+
+    # 모델 로드: yolov8m (medium)
+    model = YOLO("yolov8m.pt").to(device)
+
+    # 학습 파라미터 최적화 설정
+    train_params = {
+        'data': "/home/t25119/aiLab/labels_detect_cls/data/version1/data.yaml",
+        'epochs': 100,                # 충분한 에폭 증가
+        'imgsz': 2048,                # 고해상도로 안정적 검출
+        'batch': 4,                   # VRAM 상황에 맞춰 조정
+        'device': device,
+        'optimizer': 'SGD',           # 모멘텀 활성화된 SGD 사용
+        'lr0': 0.01,                  # 초기 학습률
+        'lrf': 0.2,                   # 최종 학습률 비율
+        'momentum': 0.937,            # SGD 모멘텀
+        'weight_decay': 0.0005,       # 가중치 감쇠
+        'warmup_epochs': 3,           # 워밍업 에폭
+        'warmup_momentum': 0.8,
+        'warmup_bias_lr': 0.1,
+        'augment': True,              # 데이터 증강 활성화
+        'cos_lr': True,               # Cosine Annealing LR 스케줄러
+        'patience': 25,               # Early stopping patience
+        'project': "/home/t25119/aiLab/labels_detect_cls/model",
+        'name': "label43_yolov8m_2048",
+        # 추가 하이퍼파라미터 옵션
+        'hsv_h': 0.015,               # HSV 색상 변형 범위
+        'hsv_s': 0.7,
+        'hsv_v': 0.4,
+        'degrees': 5.0,               # 회전 범위
+        'translate': 0.1,             # 이동 범위
+        'scale': 0.5,                 # 축소/확대 범위
+        'shear': 2.0,
+        'flipud': 0.0,                # 수직 뒤집기 비율
+        'fliplr': 0.5,                # 수평 뒤집기 비율
+        'mosaic': True,               # 모자이크 증강
+        'mixup': 0.3,                 # MixUp 증강 비율
+        # Early stopping은 patience로 자동 적용
+    }
+
+    # 학습 실행
+    model.train(**train_params)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ai_train/label/laundry_labels_cls.pt b/ai_train/label/laundry_labels_cls.pt
new file mode 100644
index 000000000..450fa4199
Binary files /dev/null and b/ai_train/label/laundry_labels_cls.pt differ
diff --git a/ai_train/label/model/laundry_data_2_yolov8m_2048_train/args.yaml b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/args.yaml
new file mode 100644
index 000000000..edd4005b5
--- /dev/null
+++ b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: yolov8m.pt
+data: /home/t25119/aiLab/labels_detect_cls/data/laundry_data_2/data.yaml
+epochs: 150
+time: null
+patience: 15
+batch: 4
+imgsz: 2048
+save: true
+save_period: -1
+cache: false
+device: cuda:0
+workers: 8
+project: /home/t25119/aiLab/labels_detect_cls/model
+name: laundry_data_2_yolov8m_2048_train
+exist_ok: false
+pretrained: true
+optimizer: SGD
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: true
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.012
+lrf: 0.2
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.5
+hsv_v: 0.4
+degrees: 5.0
+translate: 0.1
+scale: 0.8
+shear: 2.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 0.8
+mixup: 0.1
+copy_paste: 0.1
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/t25119/aiLab/labels_detect_cls/model/laundry_data_2_yolov8m_2048_train
diff --git a/ai_train/label/model/laundry_data_2_yolov8m_2048_train/events.out.tfevents.1747654063.ceprj.281903.0 b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/events.out.tfevents.1747654063.ceprj.281903.0
new file mode 100644
index 000000000..a07ff493b
Binary files /dev/null and b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/events.out.tfevents.1747654063.ceprj.281903.0 differ
diff --git a/ai_train/label/model/laundry_data_2_yolov8m_2048_train/labels.jpg b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/labels.jpg
new file mode 100644
index 000000000..8d8484fce
Binary files /dev/null and b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/labels.jpg differ
diff --git a/ai_train/label/model/laundry_data_2_yolov8m_2048_train/labels_correlogram.jpg b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/labels_correlogram.jpg
new file mode 100644
index 000000000..786bb20b9
Binary files /dev/null and b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/labels_correlogram.jpg differ
diff --git a/ai_train/label/model/laundry_data_2_yolov8m_2048_train/results.csv b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/results.csv
new file mode 100644
index 000000000..b1a34b78f
--- /dev/null
+++ b/ai_train/label/model/laundry_data_2_yolov8m_2048_train/results.csv
@@ -0,0 +1,62 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 1.7773,                  3.759,                 1.6957,                0.47375,                0.55609,                0.48547,                0.24793,                 1.6852,                 1.7362,                 1.9188,               0.070678,              0.0039984,              0.0039984
+                      2,                 1.5335,                 1.5444,                 1.4059,                0.64841,                0.47878,                0.57619,                0.32478,                 1.4213,                 1.4883,                 1.7359,               0.041344,              0.0079977,              0.0079977
+                      3,                 1.5211,                 1.5293,                 1.4147,                0.67014,                0.49081,                0.54911,                0.28468,                 1.5539,                 1.6238,                 1.8717,               0.012007,               0.011994,               0.011994
+                      4,                 1.5285,                 1.4725,                 1.3851,                0.50776,                0.46254,                0.47858,                0.24907,                 1.6321,                 1.6251,                 2.0249,               0.011991,               0.011991,               0.011991
+                      5,                 1.4847,                  1.328,                 1.3523,                0.69947,                  0.566,                0.59808,                0.27413,                 1.7601,                 1.4143,                 2.1537,               0.011983,               0.011983,               0.011983
+                      6,                 1.4492,                 1.2363,                 1.3234,                0.56886,                0.61249,                0.59797,                0.36328,                 1.5036,                 1.2785,                 1.8345,               0.011974,               0.011974,               0.011974
+                      7,                 1.3955,                  1.145,                 1.3051,                0.61626,                0.57783,                0.60822,                0.32416,                 1.6551,                 1.2894,                   2.01,               0.011962,               0.011962,               0.011962
+                      8,                 1.3927,                 1.0958,                 1.2884,                 0.6216,                0.66352,                0.66219,                0.41452,                 1.4814,                 1.1114,                  1.856,               0.011949,               0.011949,               0.011949
+                      9,                 1.3649,                 1.0504,                 1.2739,                 0.6006,                0.69387,                0.66033,                0.40108,                 1.5243,                 1.0824,                  1.893,               0.011933,               0.011933,               0.011933
+                     10,                 1.3595,                 1.0044,                  1.259,                 0.6961,                 0.6505,                0.64826,                0.40725,                 1.4927,                 1.1053,                 1.8891,               0.011915,               0.011915,               0.011915
+                     11,                 1.3555,                0.97488,                 1.2544,                0.74893,                0.67723,                0.68543,                0.42934,                 1.4988,                 1.0111,                 1.8852,               0.011895,               0.011895,               0.011895
+                     12,                 1.3165,                0.92394,                  1.246,                0.69042,                0.67396,                0.69942,                0.45017,                 1.4812,                0.99953,                 1.8638,               0.011873,               0.011873,               0.011873
+                     13,                 1.3196,                0.89887,                 1.2382,                0.70878,                0.67947,                0.69323,                0.45701,                 1.3575,                0.93299,                  1.766,               0.011849,               0.011849,               0.011849
+                     14,                 1.3164,                0.90038,                 1.2369,                0.79717,                0.65717,                0.73625,                0.45737,                 1.4885,                 0.9322,                 1.9021,               0.011823,               0.011823,               0.011823
+                     15,                 1.3212,                0.86724,                 1.2201,                0.76053,                0.73008,                0.73549,                0.47494,                 1.3774,                0.90578,                 1.7459,               0.011795,               0.011795,               0.011795
+                     16,                 1.2882,                0.82878,                 1.2215,                0.70473,                0.72276,                0.73719,                0.46296,                 1.4422,                0.89313,                 1.8706,               0.011765,               0.011765,               0.011765
+                     17,                  1.289,                0.82779,                 1.2047,                0.80161,                0.67643,                0.71893,                0.46666,                 1.4126,                0.91742,                 1.8029,               0.011733,               0.011733,               0.011733
+                     18,                 1.2881,                0.80276,                  1.204,                 0.7542,                0.68988,                0.72175,                0.47099,                 1.4372,                0.87439,                 1.8247,               0.011699,               0.011699,               0.011699
+                     19,                 1.2548,                0.75883,                 1.1965,                0.75454,                0.64107,                0.69753,                0.46904,                 1.3582,                0.90284,                 1.7577,               0.011663,               0.011663,               0.011663
+                     20,                  1.246,                0.75933,                 1.1942,                0.71165,                0.69996,                0.71044,                0.47372,                 1.3703,                0.84018,                 1.7776,               0.011625,               0.011625,               0.011625
+                     21,                 1.2567,                0.75739,                 1.1917,                0.83645,                 0.6896,                0.75238,                0.49844,                 1.3837,                 0.8118,                 1.7649,               0.011585,               0.011585,               0.011585
+                     22,                 1.2318,                0.73612,                  1.183,                0.78185,                0.67547,                0.74424,                0.50125,                 1.3698,                0.88271,                 1.7988,               0.011543,               0.011543,               0.011543
+                     23,                 1.2419,                0.72869,                 1.1854,                0.76647,                0.69734,                 0.7506,                0.50592,                 1.3474,                0.87051,                 1.8201,               0.011499,               0.011499,               0.011499
+                     24,                 1.2524,                0.71022,                 1.1681,                0.85103,                0.65597,                0.73582,                 0.4825,                 1.4261,                0.89142,                 1.8314,               0.011454,               0.011454,               0.011454
+                     25,                 1.2235,                0.72325,                 1.1821,                0.74238,                0.71535,                0.73475,                0.48605,                 1.3968,                0.83903,                 1.8398,               0.011406,               0.011406,               0.011406
+                     26,                  1.209,                0.68358,                 1.1639,                0.74362,                0.72168,                0.74458,                0.49429,                 1.3678,                 0.8066,                 1.8194,               0.011357,               0.011357,               0.011357
+                     27,                 1.1921,                0.67201,                 1.1546,                0.80599,                0.70942,                0.75041,                0.51694,                 1.3369,                0.76431,                  1.757,               0.011306,               0.011306,               0.011306
+                     28,                  1.202,                0.67108,                 1.1542,                0.73901,                0.72879,                0.73349,                0.49833,                 1.2951,                0.79092,                 1.7114,               0.011253,               0.011253,               0.011253
+                     29,                 1.1816,                0.65333,                 1.1534,                0.72377,                  0.756,                0.74825,                0.52012,                 1.3009,                 0.7673,                 1.7554,               0.011198,               0.011198,               0.011198
+                     30,                 1.2006,                0.64647,                 1.1489,                0.80057,                0.70194,                0.71023,                 0.4978,                 1.3015,                0.78166,                   1.72,               0.011142,               0.011142,               0.011142
+                     31,                 1.2007,                0.64935,                 1.1485,                0.68987,                0.71719,                0.73089,                0.50871,                  1.315,                 0.8031,                 1.7318,               0.011083,               0.011083,               0.011083
+                     32,                 1.1842,                0.63978,                 1.1406,                0.80398,                0.71376,                0.75391,                0.51959,                 1.3153,                0.77245,                 1.7395,               0.011023,               0.011023,               0.011023
+                     33,                 1.1747,                0.63192,                 1.1317,                0.84393,                0.73535,                0.76126,                0.52696,                 1.3273,                0.75244,                 1.7625,               0.010962,               0.010962,               0.010962
+                     34,                 1.1638,                0.62715,                 1.1252,                0.80668,                0.70091,                0.75194,                0.51768,                 1.3583,                0.78652,                 1.8046,               0.010898,               0.010898,               0.010898
+                     35,                 1.1583,                0.61525,                 1.1237,                0.75047,                0.67668,                0.72539,                0.50841,                  1.308,                0.78074,                 1.7622,               0.010834,               0.010834,               0.010834
+                     36,                 1.1516,                0.61424,                 1.1291,                0.79033,                0.71312,                0.74736,                0.52046,                 1.3114,                0.75931,                 1.7636,               0.010767,               0.010767,               0.010767
+                     37,                 1.1432,                0.60804,                 1.1168,                0.75908,                0.70344,                0.72148,                0.50468,                 1.3085,                0.79944,                 1.7645,               0.010699,               0.010699,               0.010699
+                     38,                 1.1427,                0.60555,                 1.1122,                0.79275,                0.69368,                0.74433,                0.51562,                 1.2961,                0.76338,                 1.7582,               0.010629,               0.010629,               0.010629
+                     39,                 1.1279,                0.58439,                 1.1075,                0.82199,                0.71185,                0.74274,                0.51369,                 1.2929,                0.76474,                 1.7434,               0.010558,               0.010558,               0.010558
+                     40,                 1.1455,                0.59111,                 1.1172,                0.83901,                0.67625,                0.72543,                0.50828,                 1.2849,                0.78696,                 1.7419,               0.010486,               0.010486,               0.010486
+                     41,                 1.1488,                0.59521,                 1.1167,                0.80909,                0.67363,                0.73938,                0.52151,                 1.2905,                0.77356,                  1.741,               0.010412,               0.010412,               0.010412
+                     42,                 1.1075,                0.57364,                 1.1029,                0.71541,                0.73598,                0.75321,                0.52519,                 1.3024,                0.75234,                 1.7654,               0.010336,               0.010336,               0.010336
+                     43,                 1.1208,                0.57587,                  1.103,                 0.7681,                0.72352,                0.72179,                0.50783,                 1.3081,                0.76423,                 1.7882,                0.01026,                0.01026,                0.01026
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new file mode 100644
index 000000000..e7ad1ee02
--- /dev/null
+++ b/ai_train/label/model/yolov8m_1600_optimized_pre_final/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: yolov8m.pt
+data: /home/t25119/aiLab/labels_detect_cls/data/laundry_data_2/data.yaml
+epochs: 100
+time: null
+patience: 20
+batch: 2
+imgsz: 1600
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+device: cuda:0
+workers: 4
+project: /home/t25119/aiLab/labels_detect_cls/model
+name: yolov8m_1600_optimized_pre_final
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+tracker: botsort.yaml
+save_dir: /home/t25119/aiLab/labels_detect_cls/model/yolov8m_1600_optimized_pre_final
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index 000000000..0ce707219
--- /dev/null
+++ b/ai_train/label/model/yolov8m_1600_optimized_pre_final/results.csv
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+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
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new file mode 100644
index 000000000..1a702d0d1
--- /dev/null
+++ b/ai_train/label/model/yolov8m_1600_optimized_pre_final2/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: yolov8m.pt
+data: /home/t25119/aiLab/labels_detect_cls/data/laundry_data_2/data.yaml
+epochs: 100
+time: null
+patience: 5
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+device: cuda:0
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+project: /home/t25119/aiLab/labels_detect_cls/model
+name: yolov8m_1600_optimized_pre_final2
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index 000000000..b4fe937da
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diff --git a/ai_train/label/model/yolov8m_1600_optimized_pre_final2/labels_correlogram.jpg b/ai_train/label/model/yolov8m_1600_optimized_pre_final2/labels_correlogram.jpg
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diff --git a/ai_train/label/model/yolov8m_1600_optimized_pre_final2/results.csv b/ai_train/label/model/yolov8m_1600_optimized_pre_final2/results.csv
new file mode 100644
index 000000000..7b7539b74
--- /dev/null
+++ b/ai_train/label/model/yolov8m_1600_optimized_pre_final2/results.csv
@@ -0,0 +1,23 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 1.7456,                 3.3023,                 1.6721,                0.52361,                0.50825,                0.50638,                 0.2847,                 1.4593,                 1.5409,                 1.6239,               0.070006,              0.0033327,              0.0033327
+                      2,                 1.4358,                 1.4958,                 1.3352,                0.63796,                0.59065,                0.58587,                 0.2838,                 1.6498,                 1.4045,                 1.7872,               0.040004,              0.0066647,              0.0066647
+                      3,                 1.4272,                 1.3999,                 1.3203,                0.70351,                0.54681,                0.59995,                0.32051,                 1.5378,                 1.4747,                 1.7462,              0.0099979,              0.0099915,              0.0099915
+                      4,                  1.413,                 1.3677,                 1.3052,                0.62058,                0.56029,                0.59004,                0.29821,                 1.5533,                 1.3773,                 1.7473,              0.0099822,              0.0099822,              0.0099822
+                      5,                 1.3696,                 1.2483,                 1.2715,                0.72462,                0.59825,                0.66224,                0.40205,                 1.4612,                 1.1737,                 1.6248,              0.0099685,              0.0099685,              0.0099685
+                      6,                 1.3463,                 1.1389,                 1.2415,                 0.7169,                0.59119,                0.67613,                0.41378,                 1.4901,                 1.1323,                 1.6776,              0.0099508,              0.0099508,              0.0099508
+                      7,                 1.3275,                 1.0804,                 1.2174,                0.73975,                 0.6292,                0.66387,                0.36154,                 1.5822,                 1.1189,                 1.7909,              0.0099291,              0.0099291,              0.0099291
+                      8,                 1.3076,                  1.024,                 1.2103,                0.63667,                0.67244,                0.67871,                0.41065,                 1.5453,                 1.0807,                 1.7142,              0.0099037,              0.0099037,              0.0099037
+                      9,                 1.2772,                 0.9535,                 1.1845,                0.60921,                0.68148,                0.69342,                0.43912,                 1.5162,                  1.101,                 1.7326,              0.0098743,              0.0098743,              0.0098743
+                     10,                 1.2736,                0.91888,                 1.1736,                 0.8188,                0.65482,                0.75078,                0.48282,                 1.4071,                0.93464,                 1.6523,              0.0098412,              0.0098412,              0.0098412
+                     11,                  1.252,                0.89536,                 1.1696,                0.65738,                0.64685,                 0.6802,                0.39464,                 1.6118,                 1.0211,                 1.8226,              0.0098042,              0.0098042,              0.0098042
+                     12,                 1.2492,                0.85996,                 1.1663,                0.68808,                0.67391,                0.71259,                0.45445,                 1.4251,                0.97536,                 1.6192,              0.0097635,              0.0097635,              0.0097635
+                     13,                 1.2387,                0.83942,                 1.1488,                0.72497,                0.70008,                0.74384,                0.48839,                 1.4195,                0.89147,                 1.6578,              0.0097191,              0.0097191,              0.0097191
+                     14,                 1.2241,                0.80497,                 1.1422,                0.75208,                0.67886,                0.74227,                0.46107,                 1.4544,                0.89753,                 1.6643,               0.009671,               0.009671,               0.009671
+                     15,                 1.2269,                0.79902,                 1.1315,                0.72518,                0.72882,                0.74311,                0.49023,                 1.3775,                0.87606,                 1.5877,              0.0096193,              0.0096193,              0.0096193
+                     16,                 1.2094,                0.77547,                 1.1293,                0.66483,                0.76175,                0.74832,                0.48515,                 1.3863,                0.80824,                 1.6286,               0.009564,               0.009564,               0.009564
+                     17,                 1.2239,                0.75864,                 1.1213,                0.86009,                0.69384,                0.76133,                0.49348,                 1.4122,                 0.8494,                 1.6238,              0.0095052,              0.0095052,              0.0095052
+                     18,                 1.1885,                0.72808,                  1.114,                 0.7038,                0.71755,                0.73056,                0.47243,                 1.4148,                0.85339,                 1.6582,               0.009443,               0.009443,               0.009443
+                     19,                 1.1939,                 0.7121,                 1.1073,                0.77521,                0.67244,                0.71367,                0.45298,                 1.4397,                 0.9151,                 1.6608,              0.0093773,              0.0093773,              0.0093773
+                     20,                 1.1851,                0.69984,                 1.0981,                0.72673,                0.65148,                0.69912,                0.46329,                 1.3919,                0.87547,                 1.6262,              0.0093083,              0.0093083,              0.0093083
+                     21,                 1.1709,                0.70112,                 1.0946,                0.77189,                0.64503,                0.71914,                0.46184,                 1.4469,                0.84629,                 1.6848,              0.0092361,              0.0092361,              0.0092361
+                     22,                 1.1687,                0.68247,                 1.0925,                0.75019,                0.65674,                0.72633,                0.49584,                 1.3697,                0.84486,                 1.6167,              0.0091606,              0.0091606,              0.0091606
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diff --git a/ai_train/label/model/yolov8m_1600_optimized_pre_final2/weights/best.pt b/ai_train/label/model/yolov8m_1600_optimized_pre_final2/weights/best.pt
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diff --git a/ai_train/label/model/yolov8m_1600_optimized_pre_final2/weights/last.pt b/ai_train/label/model/yolov8m_1600_optimized_pre_final2/weights/last.pt
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diff --git a/ai_train/label/test/symbol_test.py b/ai_train/label/test/symbol_test.py
new file mode 100644
index 000000000..59e7f91cd
--- /dev/null
+++ b/ai_train/label/test/symbol_test.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+# test_performance_summary_detailed.py
+
+import time
+import torch
+import yaml
+from ultralytics import YOLO
+
+# ——————————————————————————————————————————————
+# 유저 설정 부분
+MODEL_WEIGHTS = '/home/t25119/aiLab/labels_detect_cls/model/pre_final_yolov8m_2048/weights/best.pt'
+DATA_YAML     = '/home/t25119/aiLab/rules/data/symbol/data.yaml'
+IMG_SIZE      = 2048 #1600 # 1056
+CONF_THRESH   = 0.3   # 전 클래스 공통 confidence threshold
+# ——————————————————————————————————————————————
+
+def print_table(rows, headers):
+    # 컬럼 너비 계산
+    col_widths = [max(len(str(x)) for x in col) for col in zip(*([headers] + rows))]
+    # 헤더
+    header_line = " | ".join(h.ljust(col_widths[i]) for i, h in enumerate(headers))
+    sep_line    = "-+-".join("-" * col_widths[i] for i in range(len(headers)))
+    print(header_line)
+    print(sep_line)
+    # 데이터 행
+    for row in rows:
+        print(" | ".join(str(row[i]).ljust(col_widths[i]) for i in range(len(row))))
+
+
+def evaluate_symbol(model_path: str, data_yaml: str) -> dict:
+    model = YOLO(model_path)
+    with open(data_yaml) as f:
+        data = yaml.safe_load(f)
+    names = data['names']
+
+    results = model.val(
+        data=data_yaml,
+        split='test',
+        imgsz=IMG_SIZE,
+        conf=CONF_THRESH,
+        device='cuda:0' if torch.cuda.is_available() else 'cpu',
+        verbose=False
+    )
+
+    # ── (1) 전체 메트릭 추출 ─────────────────────────
+    P, R, mAP50, mAP5095 = results.box.mean_results()
+    inference_time_ms = results.speed.get('inference', 0.0)
+
+    # ── (2) 카테고리별 mAP50 ─────────────────────────
+    ap_map = { idx: float(ap) for idx, ap in zip(results.ap_class_index, results.maps) }
+    per_class = {
+        name: ap_map.get(i, 0.0)
+        for i, name in enumerate(names)
+    }
+
+    return {
+        "accuracy":          mAP50,
+        "precision":         P,
+        "recall":            R,
+        "mAP50-95":          mAP5095,
+        "inference_time_ms": inference_time_ms,
+        "per_class":         per_class
+    }
+
+
+
+def main():
+    # 디바이스 정보
+    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+    print(f"실행 디바이스: {device}\n")
+
+    # 평가 실행
+    metrics = evaluate_symbol(MODEL_WEIGHTS, DATA_YAML)
+
+    # 전체 지표 테이블 출력
+    rows = [
+        ("정확도 (mAP50)", f"{metrics['accuracy']*100:6.2f}%"),
+        ("정밀도 (Precision)",  f"{metrics['precision']*100:6.2f}%"),
+        ("재현율 (Recall)",     f"{metrics['recall']*100:6.2f}%"),
+        ("mAP50-95",           f"{metrics['mAP50-95']*100:6.2f}%"),
+        ("추론 시간 (ms/img)",  f"{metrics['inference_time_ms']:.1f} ms"),
+    ]
+    print("=== 전체 성능 지표 ===")
+    print_table(rows, ("메트릭", "값"))
+    print()
+
+    # 카테고리별 정확도 테이블 출력
+    per = metrics['per_class']
+    rows = [(name, f"{ap*100:6.2f}%") for name, ap in per.items()]
+    print("=== 카테고리별 mAP50 (정확도) ===")
+    print_table(rows, ("카테고리", "정확도"))
+    print()
+
+if __name__ == "__main__":
+    main()
diff --git a/ai_train/label/test/test_and_visualize_comp.py b/ai_train/label/test/test_and_visualize_comp.py
new file mode 100644
index 000000000..aa0f449bd
--- /dev/null
+++ b/ai_train/label/test/test_and_visualize_comp.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+# test_and_visualize_gpu.py
+
+import os
+import cv2
+import torch
+import yaml
+import numpy as np
+import csv
+from tqdm import tqdm
+from ultralytics import YOLO
+from pathlib import Path
+
+# ——————————————————————————————————————————————
+# 유저 설정 부분
+MODEL_WEIGHTS = '/home/t25119/aiLab/labels_detect_cls/model/luandry_data_1_yolov8l_640_train/weights/best.pt'
+DATA_YAML     = '/home/t25119/aiLab/labels_detect_cls/data/luandry_data_1/data.yaml'
+IMAGE_DIR     = '/home/t25119/aiLab/labels_detect_cls/data/luandry_data_1/test/images'
+LABEL_DIR     = '/home/t25119/aiLab/labels_detect_cls/data/luandry_data_1/test/labels'
+OUTPUT_DIR    = '/home/t25119/aiLab/labels_detect_cls/test/output/luandry_data_1_yolov8l_640_pred_test2'
+CONF_THRESH   = 0.3
+IMG_SIZE      = 640
+# ——————————————————————————————————————————————
+
+# 0) GPU 사용 지정
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+print(f"Running on device: {device}")
+
+# 1) 모델 로드 & CUDA로 이동
+model = YOLO(MODEL_WEIGHTS).to(device)
+
+# 전체 평가(metrics)
+metrics = model.val(data=DATA_YAML, imgsz=IMG_SIZE, conf=CONF_THRESH, device=device)
+print(metrics)
+
+# 2) 이미지 리스트 수집 및 출력
+image_paths = sorted(Path(IMAGE_DIR).glob('*.*'))
+total_images = len(image_paths)
+print(f"Total number of test images: {total_images}")
+
+# 3) 출력 디렉토리 및 예측 저장 리스트 초기화
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+predictions = []  # (image_name, class_id, confidence, x1, y1, x2, y2)
+
+# 컬러 정의 (BGR)
+GT_COLOR   = (0, 0, 255)
+PRED_COLOR = (0, 255, 0)
+
+# 4) 이미지별 시각화 및 예측 저장
+for img_path in tqdm(image_paths):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    # 4.1) GT 박스
+    label_path = Path(LABEL_DIR) / (img_path.stem + '.txt')
+    if label_path.exists():
+        for line in open(label_path, 'r'):
+            tokens = line.split()
+            if len(tokens) < 5:
+                continue
+            cls = int(float(tokens[0]))
+            xc, yc, bw, bh = map(float, tokens[1:5])
+            x1 = int((xc - bw/2) * w)
+            y1 = int((yc - bh/2) * h)
+            x2 = int((xc + bw/2) * w)
+            y2 = int((yc + bh/2) * h)
+            cv2.rectangle(img, (x1, y1), (x2, y2), GT_COLOR, 2)
+            cv2.putText(img, f"gt:{cls}", (x1, y1 - 5),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, GT_COLOR, 1)
+
+    # 4.2) 예측 박스 (개선된 파라미터 반영)
+    results = model.predict(
+        source=str(img_path),
+        imgsz=IMG_SIZE,
+        conf=0.03,            # 낮은 threshold로 Recall ↑
+        iou=0.35,             # 완만한 NMS
+        max_det=1000,         # 탐지 가능한 박스 수 ↑
+        agnostic_nms=True,    # 클래스 간 억압 방지
+        augment=True,         # TTA
+        device=device,
+        verbose=False
+    )
+    r = results[0]
+    for box, cls_id, conf in zip(r.boxes.xyxy, r.boxes.cls, r.boxes.conf):
+        x1, y1, x2, y2 = map(int, box.cpu().numpy())
+        # 시각화
+        cv2.rectangle(img, (x1, y1), (x2, y2), PRED_COLOR, 2)
+        cv2.putText(img, f"p:{int(cls_id)} {conf:.2f}", (x1, y2 + 15),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, PRED_COLOR, 1)
+        # 예측 결과 저장
+        predictions.append([
+            img_path.name,
+            int(cls_id),
+            float(conf),
+            x1, y1, x2, y2
+        ])
+
+    # 4.3) 시각화 이미지 저장
+    out_path = Path(OUTPUT_DIR) / img_path.name
+    cv2.imwrite(str(out_path), img)
+
+# 5) 예측 결과 CSV로 저장
+csv_path = Path(OUTPUT_DIR) / 'predictions.csv'
+with open(csv_path, 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow(['image', 'class_id', 'confidence', 'x1', 'y1', 'x2', 'y2'])
+    writer.writerows(predictions)
+print(f"✔ Predictions saved to '{csv_path}'")
+print(f"✔ Visualizations saved to '{OUTPUT_DIR}'")
diff --git a/ai_train/label/test/test_and_visualize_gpu.py b/ai_train/label/test/test_and_visualize_gpu.py
new file mode 100644
index 000000000..98d9dff6b
--- /dev/null
+++ b/ai_train/label/test/test_and_visualize_gpu.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+# test_and_visualize_gpu.py
+
+import os
+import cv2
+import torch
+import yaml
+import numpy as np
+from tqdm import tqdm
+from ultralytics import YOLO
+from pathlib import Path
+
+# ——————————————————————————————————————————————
+# 유저 설정 부분
+MODEL_WEIGHTS = '/home/t25119/aiLab/labels_detect_cls/model/label_detect_yolov8m_optimized/weights/best.pt'
+DATA_YAML     = '/home/t25119/aiLab/labels_detect_cls/data/version1/data.yaml'
+IMAGE_DIR     = '/home/t25119/aiLab/labels_detect_cls/data/version1/test/images'
+LABEL_DIR     = '/home/t25119/aiLab/labels_detect_cls/data/version1/test/labels'
+OUTPUT_DIR    = '/home/t25119/aiLab/labels_detect_cls/test/output/yolov8m_1600_pred_test'
+CONF_THRESH   = 0.1
+IMG_SIZE      = 1600
+# ——————————————————————————————————————————————
+
+# 0) GPU 사용 지정
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+print(f"Running on device: {device}")
+
+# 1) 모델 로드 & CUDA로 이동
+model = YOLO(MODEL_WEIGHTS).to(device)
+
+# 전체 평가(metrics)
+metrics = model.val(data=DATA_YAML, imgsz=IMG_SIZE, conf=CONF_THRESH, device=device)
+print(metrics)
+
+# 컬러 정의 (BGR)
+GT_COLOR   = (0, 0, 255)
+PRED_COLOR = (0, 255, 0)
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# 2) 이미지별 시각화
+for img_path in tqdm(sorted(Path(IMAGE_DIR).glob('*.*'))):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    # 2.1) GT 박스 (YOLO txt 포맷: cls xc yc w h + 추가 토큰 가능)
+    label_path = Path(LABEL_DIR) / (img_path.stem + '.txt')
+    if label_path.exists():
+        for line in open(label_path, 'r'):
+            tokens = line.split()
+            if len(tokens) < 5:
+                continue  # 좌표 정보가 부족하면 스킵
+            cls = int(float(tokens[0]))
+            xc, yc, bw, bh = map(float, tokens[1:5])
+            x1 = int((xc - bw/2) * w)
+            y1 = int((yc - bh/2) * h)
+            x2 = int((xc + bw/2) * w)
+            y2 = int((yc + bh/2) * h)
+            cv2.rectangle(img, (x1, y1), (x2, y2), GT_COLOR, 2)
+            cv2.putText(img, f"gt:{cls}", (x1, y1 - 5),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, GT_COLOR, 1)
+
+    # 2.2) 예측 박스 (GPU + 동일한 해상도)
+    results = model.predict(
+        source=str(img_path),
+        conf=CONF_THRESH,
+        imgsz=IMG_SIZE,
+        device=device,
+        verbose=False
+    )
+    r = results[0]
+    for box, cls_id, conf in zip(r.boxes.xyxy, r.boxes.cls, r.boxes.conf):
+        x1, y1, x2, y2 = map(int, box.cpu().numpy())
+        cv2.rectangle(img, (x1, y1), (x2, y2), PRED_COLOR, 2)
+        cv2.putText(img, f"p:{int(cls_id)} {conf:.2f}", (x1, y2 + 15),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, PRED_COLOR, 1)
+
+    # 2.3) 저장
+    out_path = Path(OUTPUT_DIR) / img_path.name
+    cv2.imwrite(str(out_path), img)
+
+print(f"✔ Visualizations saved to '{OUTPUT_DIR}'")
diff --git a/ai_train/label/test/test_and_visualize_with_class_thresholds.py b/ai_train/label/test/test_and_visualize_with_class_thresholds.py
new file mode 100644
index 000000000..7ab71d6ec
--- /dev/null
+++ b/ai_train/label/test/test_and_visualize_with_class_thresholds.py
@@ -0,0 +1,164 @@
+#!/usr/bin/env python3
+# test_and_visualize_with_class_thresholds.py
+
+import os
+import cv2
+import torch
+import yaml
+import numpy as np
+import csv
+from tqdm import tqdm
+from ultralytics import YOLO
+from pathlib import Path
+from torchvision.ops import nms
+
+# ——————————————————————————————————————————————
+# 유저 설정 부분
+MODEL_WEIGHTS = '/home/t25119/aiLab/rules/model/laundry_labels_cls.pt'
+DATA_YAML     = '/home/t25119/aiLab/rules/data/symbol/data.yaml'
+IMAGE_DIR     = '/home/t25119/aiLab/rules/data/symbol/test/images'
+LABEL_DIR     = '/home/t25119/aiLab/rules/data/symbol/test/labels'
+OUTPUT_DIR    = '/home/t25119/aiLab/labels_detect_cls/test/output/symbol_test_img_class_thres'
+IMG_SIZE      = 1056
+
+# 튜닝 결과: 클래스별 (conf_thresh, iou_thresh)
+CLASS_THRESHOLDS = {
+    0: (0.75, 0.45),  1: (0.13, 0.20), 2: (0.05, 0.45), 3: (0.05, 0.45),
+    4: (0.05, 0.45),  5: (0.13, 0.45), 6: (0.05, 0.45), 7: (0.13, 0.45),
+    8: (0.21, 0.45),  9: (0.67, 0.45),10: (0.13, 0.45),11: (0.36, 0.45),
+   12: (0.05, 0.45),13: (0.05, 0.45),14: (0.13, 0.45),15: (0.13, 0.45),
+   16: (0.13, 0.45),17: (0.13, 0.45),18: (0.13, 0.45),19: (0.13, 0.45),
+   20: (0.21, 0.45),21: (0.13, 0.45),22: (0.13, 0.45),23: (0.05, 0.45),
+   24: (0.13, 0.45),25: (0.13, 0.45),26: (0.13, 0.45),27: (0.52, 0.45),
+   28: (0.05, 0.45),29: (0.05, 0.45),30: (0.00, 0.20),31: (0.05, 0.45),
+   32: (0.59, 0.45),33: (0.52, 0.45),34: (0.75, 0.20),35: (0.13, 0.45),
+   36: (0.05, 0.45),37: (0.13, 0.45),38: (0.05, 0.20),39: (0.13, 0.45),
+   40: (0.13, 0.45),41: (0.13, 0.45),42: (0.05, 0.45),
+}
+# ——————————————————————————————————————————————
+
+# 0) GPU 사용 지정
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+print(f"Running on device: {device}")
+
+# 1) 모델 로드 & CUDA로 이동
+model = YOLO(MODEL_WEIGHTS).to(device)
+
+# 2) 테스트 이미지 목록
+image_paths = sorted(Path(IMAGE_DIR).glob('*.*'))
+print(f"Total number of test images: {len(image_paths)}")
+
+# 3) 출력 준비
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+predictions = []  # [img_name, class_id, conf, x1, y1, x2, y2]
+
+GT_COLOR   = (0, 0, 255)
+PRED_COLOR = (0, 255, 0)
+
+# 4) 이미지별 처리
+for img_path in tqdm(image_paths, desc="Test & visualize"):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    # 4.1) GT 박스 그리기
+    label_path = Path(LABEL_DIR) / (img_path.stem + '.txt')
+    if label_path.exists():
+        for line in open(label_path, 'r'):
+            c, xc, yc, bw, bh = map(float, line.split())
+            x1 = int((xc - bw/2) * w)
+            y1 = int((yc - bh/2) * h)
+            x2 = int((xc + bw/2) * w)
+            y2 = int((yc + bh/2) * h)
+            cv2.rectangle(img, (x1, y1), (x2, y2), GT_COLOR, 2)
+            cv2.putText(img, f"gt:{int(c)}", (x1, y1 - 5),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, GT_COLOR, 1)
+
+    # 4.2) 모델 예측 (아주 낮은 임계값으로 전체 후보 수집)
+    results = model.predict(
+        source=str(img_path),
+        imgsz=IMG_SIZE,
+        conf=0.0,    # 전부 뽑아서 뒤처리
+        iou=1.0,     # NMS 최소 억제
+        max_det=1000,
+        agnostic_nms=False,
+        device=device,
+        verbose=False
+    )
+    r = results[0]
+    boxes   = r.boxes.xyxy.cpu()        # Tensor[N,4]
+    cls_ids = r.boxes.cls.cpu().long()  # Tensor[N]
+    confs   = r.boxes.conf.cpu()        # Tensor[N]
+
+    # 4.3) 클래스별로 conf 필터 + NMS 후 최종 박스 선택
+    final_idx = []
+    for cls in torch.unique(cls_ids):
+        cls = int(cls)
+        conf_thr, iou_thr = CLASS_THRESHOLDS[cls]
+        mask = (cls_ids == cls)
+        b = boxes[mask]
+        s = confs[mask]
+        if b.numel() == 0:
+            continue
+        keep_conf = s >= conf_thr
+        b = b[keep_conf]
+        s = s[keep_conf]
+        if b.numel() == 0:
+            continue
+        keep_nms = nms(b, s, iou_thr)
+        orig_idxs = torch.nonzero(mask, as_tuple=False).squeeze(1)
+        final_idx += orig_idxs[keep_conf.nonzero().squeeze(1)][keep_nms].tolist()
+
+    # 4.4) 시각화 & CSV 저장
+    for i in final_idx:
+        x1, y1, x2, y2 = map(int, boxes[i].tolist())
+        cls_id = int(cls_ids[i])
+        conf   = float(confs[i])
+        cv2.rectangle(img, (x1, y1), (x2, y2), PRED_COLOR, 2)
+        cv2.putText(img, f"p:{cls_id} {conf:.2f}", (x1, y2 + 15),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, PRED_COLOR, 1)
+        predictions.append([
+            img_path.name, cls_id, conf, x1, y1, x2, y2
+        ])
+
+    cv2.imwrite(str(Path(OUTPUT_DIR) / img_path.name), img)
+
+# 5) CSV 저장
+csv_path = Path(OUTPUT_DIR) / 'predictions.csv'
+with open(csv_path, 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow(['image','class_id','confidence','x1','y1','x2','y2'])
+    writer.writerows(predictions)
+
+print(f"\n✔ Predictions saved to '{csv_path}'")
+print(f"✔ Visualizations saved to '{OUTPUT_DIR}'")
+
+# —————————————— 테스트셋 평가 지표 출력 ——————————————
+print("\n=== TEST SET EVALUATION ===")
+results = model.val(
+    data=DATA_YAML,
+    split='test',
+    imgsz=IMG_SIZE,
+    conf=0.0,
+    iou=1.0,
+    device=device,
+    verbose=False
+)
+
+# 전체 지표
+P, R, mAP50, mAP5095 = (
+    results.metrics.precision,
+    results.metrics.recall,
+    results.metrics.map50,
+    results.metrics.map5095
+)
+print(f"Precision (all)   : {P*100:5.2f}%")
+print(f"Recall    (all)   : {R*100:5.2f}%")
+print(f"mAP50     (all)   : {mAP50*100:5.2f}%")
+print(f"mAP50–95  (all)   : {mAP5095*100:5.2f}%")
+
+# 클래스별 mAP50
+print("\n--- Class-wise mAP50 ---")
+for idx, ap in zip(results.ap_class_index, results.maps):
+    name = results.names[int(idx)]
+    print(f"{idx:02d} {name:20s}: {ap*100:5.2f}%")
+
diff --git a/ai_train/label/test/test_class_thresholds_2.py b/ai_train/label/test/test_class_thresholds_2.py
new file mode 100644
index 000000000..48a722550
--- /dev/null
+++ b/ai_train/label/test/test_class_thresholds_2.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+# test_and_visualize_with_class_thresholds.py
+
+import os
+import cv2
+import torch
+import yaml
+import numpy as np
+import csv
+from tqdm import tqdm
+from ultralytics import YOLO
+from pathlib import Path
+from torchvision.ops import nms
+
+# ——————————————————————————————————————————————
+# 유저 설정 부분
+MODEL_WEIGHTS = '/home/t25119/aiLab/labels_detect_cls/model/luandry_data_1_yolov8l_640_train/weights/best.pt'
+DATA_YAML     = '/home/t25119/aiLab/labels_detect_cls/data/luandry_data_1/data.yaml'
+IMAGE_DIR     = '/home/t25119/aiLab/labels_detect_cls/data/luandry_data_1/test/images'
+OUTPUT_DIR    = '/home/t25119/aiLab/labels_detect_cls/test/output/luandry_data_1_yolov8l_640_pred_test3'
+IMG_SIZE      = 640
+
+# 튜닝 결과: 클래스별 (conf_thresh, iou_thresh)
+CLASS_THRESHOLDS = {
+    0: (0.75, 0.45),  1: (0.13, 0.20), 2: (0.05, 0.45), 3: (0.05, 0.45),
+    4: (0.05, 0.45),  5: (0.13, 0.45), 6: (0.05, 0.45), 7: (0.13, 0.45),
+    8: (0.21, 0.45),  9: (0.67, 0.45),10: (0.13, 0.45),11: (0.36, 0.45),
+   12: (0.05, 0.45),13: (0.05, 0.45),14: (0.13, 0.45),15: (0.13, 0.45),
+   16: (0.13, 0.45),17: (0.13, 0.45),18: (0.13, 0.45),19: (0.13, 0.45),
+   20: (0.21, 0.45),21: (0.13, 0.45),22: (0.13, 0.45),23: (0.05, 0.45),
+   24: (0.13, 0.45),25: (0.13, 0.45),26: (0.13, 0.45),27: (0.52, 0.45),
+   28: (0.05, 0.45),29: (0.05, 0.45),30: (0.00, 0.20),31: (0.05, 0.45),
+   32: (0.59, 0.45),33: (0.52, 0.45),34: (0.75, 0.20),35: (0.13, 0.45),
+   36: (0.05, 0.45),37: (0.13, 0.45),38: (0.05, 0.20),39: (0.13, 0.45),
+   40: (0.13, 0.45),41: (0.13, 0.45),42: (0.05, 0.45),
+}
+# ——————————————————————————————————————————————
+
+# 0) GPU 사용 지정
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+print(f"Running on device: {device}")
+
+# 1) 모델 로드 & CUDA로 이동
+model = YOLO(MODEL_WEIGHTS).to(device)
+
+# 2) 클래스 이름 로드
+with open(DATA_YAML) as f:
+    data = yaml.safe_load(f)
+class_names = data['names']
+
+# 3) 테스트 이미지 목록
+image_paths = sorted(Path(IMAGE_DIR).glob('*.*'))
+print(f"Total number of test images: {len(image_paths)}")
+
+# 4) 출력 준비
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+predictions = []  # [img_name, class_name, conf, x1, y1, x2, y2]
+PRED_COLOR = (0, 255, 0)
+TEXT_COLOR = (0, 0, 255)  # 빨간색
+
+# 5) 이미지별 처리
+for img_path in tqdm(image_paths, desc="Test & visualize"):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    # 5.1) 모델 예측 (아주 낮은 임계값으로 전체 후보 수집)
+    results = model.predict(
+        source=str(img_path),
+        imgsz=IMG_SIZE,
+        conf=0.0,    # 전부 뽑아서 뒤처리
+        iou=1.0,     # NMS 최소 억제
+        max_det=1000,
+        agnostic_nms=False,
+        device=device,
+        verbose=False
+    )
+    r = results[0]
+    boxes   = r.boxes.xyxy.cpu()       # Tensor[N,4]
+    cls_ids = r.boxes.cls.cpu().long() # Tensor[N]
+    confs   = r.boxes.conf.cpu()       # Tensor[N]
+
+    # 5.2) 클래스별로 conf 필터 + NMS 후 최종 박스 선택
+    final_idx = []
+    for cls in torch.unique(cls_ids):
+        cls = int(cls)
+        conf_thr, iou_thr = CLASS_THRESHOLDS[cls]
+        mask = (cls_ids == cls)
+        b = boxes[mask]; s = confs[mask]
+        if b.numel() == 0: continue
+
+        # 1) conf 필터
+        keep_conf = s >= conf_thr
+        b = b[keep_conf]; s = s[keep_conf]
+        if b.numel() == 0: continue
+
+        # 2) NMS
+        keep_nms = nms(b, s, iou_thr)
+        orig_idxs = torch.nonzero(mask, as_tuple=False).squeeze(1)
+        final_idx += orig_idxs[keep_conf.nonzero().squeeze(1)][keep_nms].tolist()
+
+    # 5.3) 레이블 오프셋 초기화 (이미지별)
+    label_offsets = {}  # {박스 인덱스: 누적 레이블 수}
+
+    # 5.4) 시각화 & CSV 저장
+    for i in final_idx:
+        x1, y1, x2, y2 = map(int, boxes[i].tolist())
+        cls_id = int(cls_ids[i])
+        conf   = float(confs[i])
+        name   = class_names[cls_id]
+
+        # 박스 그리기
+        cv2.rectangle(img, (x1, y1), (x2, y2), PRED_COLOR, 2)
+
+        # 누적된 오프셋 만큼 아래로 텍스트 위치 조정
+        offset = label_offsets.get(i, 0)
+        text_x = x1
+        text_y = y2 + 30 + offset * 40  # 각 레이블마다 40px 아래로 이동
+
+        # 텍스트 그리기
+        cv2.putText(
+            img,
+            f"{name} {conf:.2f}",
+            (text_x, text_y),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1.0,         # fontScale
+            TEXT_COLOR,
+            3            # thickness
+        )
+
+        # 같은 박스 위에 다음 레이블이 겹치지 않도록 오프셋 +1
+        label_offsets[i] = offset + 1
+
+        # CSV 기록
+        predictions.append([
+            img_path.name, name, conf, x1, y1, x2, y2
+        ])
+
+    # 5.5) 결과 이미지 저장
+    out_path = Path(OUTPUT_DIR) / img_path.name
+    cv2.imwrite(str(out_path), img)
+
+# 6) CSV 저장
+csv_path = Path(OUTPUT_DIR) / 'predictions.csv'
+with open(csv_path, 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow(['image','class_name','confidence','x1','y1','x2','y2'])
+    writer.writerows(predictions)
+
+print(f"✔ Predictions saved to '{csv_path}'")
+print(f"✔ Visualizations saved to '{OUTPUT_DIR}'")
diff --git a/ai_train/label/test/test_conf_05.py b/ai_train/label/test/test_conf_05.py
new file mode 100644
index 000000000..88a9ddf6f
--- /dev/null
+++ b/ai_train/label/test/test_conf_05.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+# test_and_visualize_with_class_thresholds_fixed_conf.py
+
+import os
+import cv2
+import torch
+import yaml
+import csv
+from tqdm import tqdm
+from ultralytics import YOLO
+from pathlib import Path
+
+# ——————————————————————————————————————————————
+# 유저 설정 부분
+MODEL_WEIGHTS = '/home/t25119/aiLab/rules/model/laundry_labels_cls.pt'
+DATA_YAML     = '/home/t25119/aiLab/labels_detect_cls/data/laundry_data_2/data.yaml'
+IMAGE_DIR     = '/home/t25119/aiLab/labels_detect_cls/data/laundry_data_2/test/images'
+OUTPUT_DIR    = '/home/t25119/aiLab/labels_detect_cls/test/output/laundry_labels_cls_pred_fixed_conf_05'
+IMG_SIZE      = 1056
+CONF_THRESH   = 0.5   # 전 클래스 공통 confidence threshold
+# ——————————————————————————————————————————————
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+print(f"Running on device: {device}")
+
+# 모델 로드
+model = YOLO(MODEL_WEIGHTS).to(device)
+
+# 클래스 이름 로드
+with open(DATA_YAML) as f:
+    data = yaml.safe_load(f)
+class_names = data['names']
+
+# 이미지 리스트
+image_paths = sorted(Path(IMAGE_DIR).glob('*.*'))
+print(f"Total number of test images: {len(image_paths)}")
+
+# 출력 디렉토리 생성
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+predictions = []  # [img_name, class_name, conf, x1, y1, x2, y2]
+
+PRED_COLOR = (0, 255, 0)
+TEXT_COLOR = (0, 0, 255)
+
+# 테스트 & 시각화
+for img_path in tqdm(image_paths, desc="Test & visualize"):
+    img = cv2.imread(str(img_path))
+    h, w = img.shape[:2]
+
+    # 모델 예측: conf=0.3, iou=0.45 (디폴트)
+    results = model.predict(
+        source=str(img_path),
+        imgsz=IMG_SIZE,
+        conf=CONF_THRESH,
+        iou=0.45,
+        max_det=1000,
+        device=device,
+        verbose=False
+    )
+    r = results[0]
+    boxes   = r.boxes.xyxy.cpu().numpy().astype(int)  # (N,4)
+    cls_ids = r.boxes.cls.cpu().numpy().astype(int)   # (N,)
+    confs   = r.boxes.conf.cpu().numpy()             # (N,)
+
+    # 오프셋 초기화 (한 이미지당)
+    label_offsets = {}
+
+    for idx, (box, cls_id, conf) in enumerate(zip(boxes, cls_ids, confs)):
+        x1, y1, x2, y2 = box
+        name = class_names[cls_id]
+
+        # 박스 그리기
+        cv2.rectangle(img, (x1, y1), (x2, y2), PRED_COLOR, 2)
+
+        # 텍스트 위치 오프셋 적용
+        offset = label_offsets.get(idx, 0)
+        text_x = x1
+        text_y = y2 + 30 + offset * 40
+
+        cv2.putText(
+            img,
+            f"{name} {conf:.2f}",
+            (text_x, text_y),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1.0,          # 글씨 크기
+            TEXT_COLOR,
+            3             # 두께
+        )
+
+        # 같은 박스 위에 겹치지 않도록 오프셋 증가
+        label_offsets[idx] = offset + 1
+
+        # CSV 기록
+        predictions.append([
+            img_path.name, name, float(conf), x1, y1, x2, y2
+        ])
+
+    # 결과 이미지 저장
+    out_path = Path(OUTPUT_DIR) / img_path.name
+    cv2.imwrite(str(out_path), img)
+
+# CSV 저장
+csv_path = Path(OUTPUT_DIR) / 'predictions.csv'
+with open(csv_path, 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow(['image','class_name','confidence','x1','y1','x2','y2'])
+    writer.writerows(predictions)
+
+print(f"✔ Predictions saved to '{csv_path}'")
+print(f"✔ Visualizations saved to '{OUTPUT_DIR}'")
diff --git a/ai_train/label/test/threshold_tuning.py b/ai_train/label/test/threshold_tuning.py
new file mode 100644
index 000000000..7263269bd
--- /dev/null
+++ b/ai_train/label/test/threshold_tuning.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+# threshold_tuning_simple.py
+
+import numpy as np
+import torch
+import yaml
+from ultralytics import YOLO
+from tqdm import tqdm
+
+# ——————————————————————————————————————————————
+# 사용자 설정
+MODEL_WEIGHTS = '/home/t25119/aiLab/labels_detect_cls/model/luandry_data_1_yolov8m_1056_train/weights/best.pt'
+DATA_YAML     = '/home/t25119/aiLab/labels_detect_cls/data/luandry_data_1/data.yaml'
+IMG_SIZE      = 1056
+
+# 그리드 서치 후보 (필요에 따라 줄여 주세요)
+CONF_CANDIDATES = np.linspace(0.05, 0.75, 10)  # ← 15→10로 축소
+IOU_CANDIDATES  = np.linspace(0.20, 0.65, 8)   # ← 10→8로 축소
+FIXED_IOU       = 0.45
+
+# ——————————————————————————————————————————————
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f"▶ Running on {device}")
+
+# 1) 모델 로드
+model = YOLO(MODEL_WEIGHTS).to(device)
+
+# 2) 데이터셋 정보 읽기
+with open(DATA_YAML) as f:
+    data = yaml.safe_load(f)
+num_classes = len(data['names'])
+
+# 결과 저장 배열
+best_conf  = np.zeros(num_classes)
+best_iou   = np.full(num_classes, FIXED_IOU)
+best_map50 = np.zeros(num_classes)
+
+# 3) 클래스별 confidence 튜닝 (IoU 고정)
+print("▶ Step1: Class-wise confidence tuning (IoU=%.2f)" % FIXED_IOU)
+for conf in tqdm(CONF_CANDIDATES, desc="conf grid"):
+    # 모델 평가 (per-class mAP50 은 metrics.maps 에 배열로 담겨 있음)
+    metrics = model.val(
+        data=DATA_YAML,
+        imgsz=IMG_SIZE,
+        conf=conf,
+        iou=FIXED_IOU,
+        device=device,
+        verbose=False,
+        plots=False
+    )
+    maps = metrics.maps  # numpy array shape=(num_classes,)
+    # 각 클래스별로 더 좋으면 갱신
+    for k in range(num_classes):
+        if maps[k] > best_map50[k]:
+            best_map50[k] = maps[k]
+            best_conf[k]  = conf
+
+# 4) 클래스별 IoU 튜닝 (conf 고정)
+print("\n▶ Step2: Class-wise IoU tuning")
+for iou in tqdm(IOU_CANDIDATES, desc="iou grid"):
+    metrics = model.val(
+        data=DATA_YAML,
+        imgsz=IMG_SIZE,
+        conf=None,       # None 이면 내부에서 best_conf[k] 를 사용하도록 기본값이 None을 허용합니다.
+        iou=iou,
+        device=device,
+        verbose=False,
+        plots=False
+    )
+    maps = metrics.maps
+    for k in range(num_classes):
+        if maps[k] > best_map50[k]:
+            best_map50[k] = maps[k]
+            best_iou[k]   = iou
+
+# 5) 최종 요약 출력
+print("\n✅ 최적 임계값 요약:")
+for k, name in enumerate(data['names']):
+    print(f"  [{k:02d}] {name:20s} -> conf_thresh={best_conf[k]:.2f}, iou_thresh={best_iou[k]:.2f}, AP50={best_map50[k]:.3f}")
diff --git a/ai_train/label/train/yolov8l_1920.py b/ai_train/label/train/yolov8l_1920.py
new file mode 100644
index 000000000..b500a297f
--- /dev/null
+++ b/ai_train/label/train/yolov8l_1920.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+import os
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+# ─── Ray 2.x 세션 이름 바인딩 패치 ─────────────────────────
+try:
+    import ray.train._internal.session as _internal_session
+    if hasattr(_internal_session, 'get_session') and not hasattr(_internal_session, '_get_session'):
+        _internal_session._get_session = _internal_session.get_session
+    print("[Patch] Ray 2.x 호환성 패치 완료")
+except Exception as e:
+    print(f"[Warning] Ray 패치 실패 ({e}), RayTuneCallback 에러가 발생할 수 있음")
+# ───────────────────────────────────────────────────────────
+
+import torch
+from ultralytics import YOLO
+
+
+def main():
+    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+    print(f"[Train] Using device: {device}")
+
+    # 🔄 1) 43 클래스용 경량 모델 : yolov8s
+    model = YOLO("yolov8l.pt").to(device)
+
+    # 🔄 2) 학습 파라미터 (고해상도 + 강한 증강 + 더 많은 epoch)
+    train_params = {
+        'data': "/home/t25119/aiLab/labels_detect_cls/data/version3/data.yaml",
+        'epochs': 150,              # s 버전은 파라미터 적으니 epoch ↑
+        'imgsz': 1920,              # 고해상도로 작은 기호 살리기
+        'batch': 4,                 # 메모리 여유 → 8 권장(안 되면 4로)
+        'device': device,
+
+        # Optimizer & LR schedule
+        'optimizer': 'SGD',
+        'lr0': 0.012,               # 모델이 작으니 약간 ↑
+        'lrf': 0.2,
+        'momentum': 0.937,
+        'weight_decay': 0.0005,
+        'warmup_epochs': 3,
+        'warmup_momentum': 0.8,
+        'warmup_bias_lr': 0.1,
+        'cos_lr': True,
+
+        # Early stopping
+        'patience': 25,
+
+        # 증강(Recall 확보용) 🔄
+        'hsv_h': 0.015,
+        'hsv_s': 0.7,
+        'hsv_v': 0.4,
+        'degrees': 5.0,
+        'translate': 0.1,
+        'scale': 0.5,
+        'shear': 2.0,
+        'flipud': 0.0,
+        'fliplr': 0.5,
+        'mosaic': True,
+        'mixup': 0.3,
+
+        # 프로젝트 폴더
+        'project': "/home/t25119/aiLab/labels_detect_cls/model",
+        'name': "version3_yolov8l_1920_train",
+    }
+
+    model.train(**train_params)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ai_train/label/train/yolov8m_2048_3.py b/ai_train/label/train/yolov8m_2048_3.py
new file mode 100644
index 000000000..b2c65904e
--- /dev/null
+++ b/ai_train/label/train/yolov8m_2048_3.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+import os
+import torch
+from ultralytics import YOLO
+
+# ─── GPU 메모리 제한 & AMP 활성화 ─────────────────────────
+try:
+    torch.cuda.set_per_process_memory_fraction(0.5, device=0)
+    print("[Info] GPU 메모리 사용 한도: 50%")
+except Exception:
+    pass
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.benchmark = True
+
+# ─── Ray 2.x 호환 패치 ─────────────────────────
+try:
+    import ray.train._internal.session as _internal_session
+    if hasattr(_internal_session, 'get_session') and not hasattr(_internal_session, '_get_session'):
+        _internal_session._get_session = _internal_session.get_session
+    print("[Patch] Ray 2.x 호환성 패치 완료")
+except Exception as e:
+    print(f"[Warning] Ray 패치 실패 ({e})")
+
+def main():
+    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+    print(f"[Train] Using device: {device}")
+
+    # last.pt 로 optimizer 상태, epoch 정보까지 함께 불러옵니다.
+    model = YOLO("/home/t25119/aiLab/labels_detect_cls/model/pre_final_yolov8m_2048/weights/last.pt").to(device)
+
+    train_params = {
+        'data':          "/home/t25119/aiLab/labels_detect_cls/data/laundry_data_2/data.yaml",
+        'epochs':        100,        # 총 학습을 마칠 epochs 수
+        'imgsz':         2048,
+        'batch':         2,
+        'device':        device,
+        'workers':       4,
+        'optimizer':     'SGD',
+        'lr0':           0.01,
+        'lrf':           0.2,
+        'momentum':      0.937,
+        'weight_decay':  0.0005,
+        'warmup_epochs':    3,
+        'warmup_momentum':  0.8,
+        'warmup_bias_lr':   0.1,
+        'augment':       True,
+        'cos_lr':        True,
+        'patience':      5,
+        'half':          True,
+        'project':       "/home/t25119/aiLab/labels_detect_cls/model",
+        'name':          "pre_final_yolov8m_2048",
+        'exist_ok':      True,    # 같은 project/name 폴더 재사용
+        'resume':        True,    # 이전 체크포인트에서 이어서 학습
+        # — 이하 augmentation 하이퍼파라미터 —
+        'hsv_h':     0.015,
+        'hsv_s':     0.7,
+        'hsv_v':     0.4,
+        'degrees':   5.0,
+        'translate': 0.1,
+        'scale':     0.5,
+        'shear':     2.0,
+        'flipud':    0.0,
+        'fliplr':    0.5,
+        'mosaic':    True,
+        'mixup':     0.3,
+    }
+
+    model.train(**train_params)
+
+if __name__ == "__main__":
+    main()
diff --git a/ai_train/llm/laundry_pipeline.py b/ai_train/llm/laundry_pipeline.py
new file mode 100644
index 000000000..2e67a2664
--- /dev/null
+++ b/ai_train/llm/laundry_pipeline.py
@@ -0,0 +1,214 @@
+import os
+import json
+import cv2
+import numpy as np
+from pathlib import Path
+from ultralytics import YOLO
+import torch
+import sys
+import random
+
+# ───── 경로 설정 ─────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+STAIN_MODEL_PATH = os.path.join(BASE_DIR, "stain", "stain_cls.pt")
+LABEL_MODEL_PATH = os.path.join(BASE_DIR, "symbol", "laundry_labels_cls.pt")
+STAIN_GUIDE_PATH = os.path.join(BASE_DIR, "stain", "stain_washing_guidelines.json")
+LABEL_GUIDE_PATH = os.path.join(BASE_DIR, "symbol", "label_symbol_guide.json")
+OUT_DIR = "/home/t25119/tyoon/tyoon/ai/"
+# ───── 클래스 및 설정 ─────
+STAIN_CLASSES = [
+    "blood",
+    "coffee",
+    "earth",
+    "ink",
+    "kimchi",
+    "lipstick",
+    "mustard",
+    "oil",
+    "wine",
+]
+CLASS_CONF_TH = {
+    "blood": 0.260,
+    "coffee": 0.350,
+    "earth": 0.230,
+    "ink": 0.190,
+    "kimchi": 0.500,
+    "lipstick": 0.330,
+    "mustard": 0.160,
+    "oil": 0.360,
+    "wine": 0.100,
+}
+GLOBAL_CONF = min(CLASS_CONF_TH.values())
+LABEL_CONF = 0.5
+TOP_K = 3
+
+# ───── 모델 및 가이드 로딩 ─────
+stain_model = YOLO(STAIN_MODEL_PATH)
+label_model = YOLO(LABEL_MODEL_PATH)
+with open(STAIN_GUIDE_PATH, "r", encoding="utf-8") as f:
+    stain_guide = json.load(f)
+with open(LABEL_GUIDE_PATH, "r", encoding="utf-8") as f:
+    label_guide = json.load(f)
+
+
+# ───── stain 예측 ─────
+def predict_stain(image_path):
+    result = stain_model(image_path, conf=GLOBAL_CONF)[0]
+    classes = result.boxes.cls.cpu().numpy().astype(int)
+    probs = result.boxes.conf.cpu().numpy()
+    boxes = result.boxes.xyxy.cpu().numpy()
+
+    keep = np.array(
+        [probs[i] >= CLASS_CONF_TH[STAIN_CLASSES[cls]] for i, cls in enumerate(classes)]
+    )
+    classes, probs, boxes = classes[keep], probs[keep], boxes[keep]
+    if len(classes) == 0:
+        return [], ""
+
+    sorted_idx = probs.argsort()[::-1]
+    top3 = [
+        (STAIN_CLASSES[classes[i]], round(float(probs[i]), 3))
+        for i in sorted_idx[:TOP_K]
+    ]
+    top1_idx = sorted_idx[0]
+
+    # 시각화
+    img = cv2.imread(image_path)
+    h, w = img.shape[:2]
+    for i in range(len(classes)):
+        x1, y1, x2, y2 = map(int, boxes[i])
+        cls_id = classes[i]
+        label = f"{STAIN_CLASSES[cls_id]} {probs[i]:.2f}"
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
+        cv2.putText(
+            img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2
+        )
+
+    name = Path(image_path).stem
+    out_dir = os.path.join(OUT_DIR, "output", "stain")
+    os.makedirs(os.path.join(out_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(out_dir, "labels"), exist_ok=True)
+
+    image_out = os.path.join(out_dir, "images", f"{name}_stain.jpg")
+    label_out = os.path.join(out_dir, "labels", f"{name}.txt")
+
+    # Top-1 라벨 저장
+    with open(label_out, "w") as f:
+        i = top1_idx
+        x1, y1, x2, y2 = map(int, boxes[i])
+        xc = ((x1 + x2) / 2) / w
+        yc = ((y1 + y2) / 2) / h
+        bw = (x2 - x1) / w
+        bh = (y2 - y1) / h
+        f.write(f"{classes[i]} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}\n")
+
+    cv2.imwrite(image_out, img)
+    return top3, os.path.relpath(image_out, OUT_DIR).replace("\\", "/")
+
+
+# ───── symbol 예측 ─────
+def predict_label(image_path):
+    result = label_model.predict(
+        source=image_path,
+        imgsz=1600,
+        conf=LABEL_CONF,
+        iou=0.35,
+        max_det=1000,
+        agnostic_nms=True,
+        augment=True,
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        verbose=False,
+    )[0]
+
+    boxes = result.boxes.xyxy.cpu().numpy()
+    classes = result.boxes.cls.cpu().numpy().astype(int)
+    probs = result.boxes.conf.cpu().numpy()
+
+    img = cv2.imread(image_path)
+    h, w = img.shape[:2]
+    name = Path(image_path).stem
+
+    out_dir = os.path.join(OUT_DIR, "output", "symbol")
+    os.makedirs(os.path.join(out_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(out_dir, "labels"), exist_ok=True)
+
+    image_out = os.path.join(out_dir, "images", f"{name}_symbol.jpg")
+    label_out = os.path.join(out_dir, "labels", f"{name}.txt")
+
+    with open(label_out, "w") as f:
+        for cls_id, conf, box in zip(classes, probs, boxes):
+            x1, y1, x2, y2 = map(int, box)
+            class_name = label_model.names[cls_id]
+            label = f"{class_name} {conf:.2f}"
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(
+                img, label, (x1, y1 - 20), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), 4
+            )
+
+            xc = ((x1 + x2) / 2) / w
+            yc = ((y1 + y2) / 2) / h
+            bw = (x2 - x1) / w
+            bh = (y2 - y1) / h
+            f.write(f"{cls_id} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}\n")
+
+    cv2.imwrite(image_out, img)
+    label_names = list(set(label_model.names[cls] for cls in classes))
+    return label_names, os.path.relpath(image_out, OUT_DIR).replace("\\", "/")
+
+
+# ───── 실행 진입점 ─────
+def main():
+    if len(sys.argv) < 3:
+        print("사용법: python laundry_pipeline.py <analysis_type> <image_path>")
+        sys.exit(1)
+
+    analysis_type = sys.argv[1]
+    image_path = sys.argv[2]
+
+    if analysis_type == "stain_only":
+        top3, output_path = predict_stain(image_path)
+        output = {
+            "detected_stain": {
+                "top3": [{"class": c, "confidence": s} for c, s in top3]
+            },
+            "washing_instructions": [],
+            "output_image_path": output_path,
+        }
+
+        seen_classes = set()
+
+        for stain, _ in top3:
+            if stain in seen_classes:
+                continue  # 중복 방지
+            seen_classes.add(stain)
+
+            methods = stain_guide.get(stain, [])
+            if isinstance(methods, list) and len(methods) > 0:
+                first = methods[0]
+                remaining = methods[1:]
+                rand = random.sample(remaining, k=min(2, len(remaining)))
+                combined = [first] + rand
+            else:
+                combined = ["정보 없음"]
+
+            output["washing_instructions"].append(
+                {"class": stain, "instructions": combined}
+            )
+
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    elif analysis_type == "label_only":
+        labels, output_path = predict_label(image_path)
+        output = {
+            "detected_labels": labels,
+            "label_explanation": [label_guide.get(lbl, "정보 없음") for lbl in labels],
+            "output_image_path": output_path,
+        }
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+
+    else:
+        print("⚠️ analysis_type은 'stain_only' 또는 'label_only' 중 하나여야 합니다.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ai_train/llm/lm_test.py b/ai_train/llm/lm_test.py
new file mode 100644
index 000000000..06e21d134
--- /dev/null
+++ b/ai_train/llm/lm_test.py
@@ -0,0 +1,128 @@
+import os
+import time
+import json
+import torch
+import gc
+from dotenv import load_dotenv
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ─── 환경변수 로딩 ───
+load_dotenv()
+HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+# ─── 모델 리스트 ───
+MODELS = [
+    "kakaocorp/kanana-nano-2.1b-base",
+]
+
+# ─── 샘플 입력 ───
+SAMPLES = [
+    {
+        "stain": {"class": "blood", "advice": "찬물에 불린 후 단백질 분해 효소가 포함된 세제로 세탁하세요."},
+        "labels": [
+            "40도 이하에서 일반 세탁 가능합니다.",
+            "표백 금지: 산소계, 염소계 표백제를 모두 사용하지 마세요.",
+            "그늘에서 옷걸이나 줄에 널어 말리세요."
+        ]
+    },
+    {
+        "stain": {"class": "coffee", "advice": "즉시 찬물로 헹군 후 산소계 표백제를 사용해 세탁하세요."},
+        "labels": [
+            "30도에서 섬세 세탁하세요.",
+            "건조기 사용 금지",
+            "중간 온도에서 다림질하세요."
+        ]
+    }
+]
+
+# ─── 프롬프트 구성 ───
+def build_prompt(sample):
+    return (
+        f"다음은 옷의 얼룩과 세탁 기호에 대한 분석 결과입니다.\n"
+        f"- 얼룩 종류: {sample['stain']['class']}\n"
+        f"- 추천 세탁법: {sample['stain']['advice']}\n"
+        f"- 세탁 기호 해석: {', '.join(sample['labels'])}\n\n"
+        "위 정보를 참고하여 사용자에게 친절하고 자연스러운 세탁 가이드를 작성해주세요.\n\n답변:"
+    )
+
+# ─── 모델 벤치마크 ───
+def benchmark_model(model_name, samples):
+    print(f"\n📥 모델 로딩 중: {model_name}")
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name, token=HF_TOKEN, padding_side="left")
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # dtype 우선순위: float16 → float32 (bfloat16은 생략)
+        try:
+            model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=torch.float16,
+                trust_remote_code=True
+            ).to("cuda")
+        except (RuntimeError, ValueError) as e:
+            print("⚠️ float16 오류 → float32로 fallback")
+            model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=torch.float32,
+                trust_remote_code=True
+            ).to("cuda")
+
+    except Exception as e:
+        print(f"❌ 모델 로딩 실패: {e}")
+        return {"model": model_name, "error": str(e), "results": []}
+
+    results = []
+    for i, sample in enumerate(samples):
+        try:
+            prompt = build_prompt(sample)
+            input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to("cuda")
+
+            start = time.time()
+            output = model.generate(
+                input_ids,
+                max_new_tokens=128,
+                do_sample=True,
+                temperature=0.7,
+                top_k=50,
+                top_p=0.9,
+                pad_token_id=tokenizer.eos_token_id
+            )
+            elapsed = time.time() - start
+
+            decoded = tokenizer.decode(output[0], skip_special_tokens=True).split("답변:")[-1].strip()
+
+            results.append({
+                "input": sample,
+                "elapsed_time_sec": round(elapsed, 2),
+                "guide": decoded
+            })
+
+        except Exception as e:
+            print(f"⚠️ 샘플 {i+1} 처리 오류: {e}")
+            results.append({
+                "input": sample,
+                "error": str(e)
+            })
+
+    del model, tokenizer
+    torch.cuda.empty_cache()
+    gc.collect()
+
+    return {"model": model_name, "results": results}
+
+# ─── 실행 ───
+def run_benchmark():
+    for model in MODELS:
+        print(f"\n⏳ 모델 실행 시작: {model}")
+        try:
+            result = benchmark_model(model, SAMPLES)
+            save_path = f"benchmark_result_fixed_{model.replace('/', '_')}.json"
+            with open(save_path, "w", encoding="utf-8") as f:
+                json.dump(result, f, ensure_ascii=False, indent=2)
+            print(f"✅ 저장 완료: {save_path}")
+        except Exception as e:
+            print(f"❌ {model} 전체 처리 실패: {e}")
+
+# ─── 시작점 ───
+if __name__ == "__main__":
+    run_benchmark()
diff --git a/ai_train/preprocessing/data_preprocessing.py b/ai_train/preprocessing/data_preprocessing.py
new file mode 100644
index 000000000..36ef0ff6d
--- /dev/null
+++ b/ai_train/preprocessing/data_preprocessing.py
@@ -0,0 +1,44 @@
+import os
+import cv2
+import numpy as np
+
+def crop_pad_resize(image, target_size=640):
+    h, w, _ = image.shape
+
+    # 중심 기준으로 정사각형 crop
+    side = min(h, w)
+    center_x, center_y = w // 2, h // 2
+    x1 = max(center_x - side // 2, 0)
+    y1 = max(center_y - side // 2, 0)
+    x2 = x1 + side
+    y2 = y1 + side
+    cropped = image[y1:y2, x1:x2]
+
+    # 정사각형으로 resize
+    resized = cv2.resize(cropped, (target_size, target_size))
+    return resized
+
+# ✅ 전체 폴더 경로들
+base_path = "/home/t25119/aiLab/data/stain"
+split_list = ["train", "val", "test"]
+target_size = 640
+
+for split in split_list:
+    input_dir = os.path.join(base_path, split, "images")
+    output_dir = os.path.join(base_path + "_preprocessed", split, "images")
+    os.makedirs(output_dir, exist_ok=True)
+
+    image_files = [f for f in os.listdir(input_dir) if f.lower().endswith((".jpg", ".jpeg", ".png"))]
+
+    for fname in image_files:
+        img_path = os.path.join(input_dir, fname)
+        image = cv2.imread(img_path)
+
+        if image is None:
+            print(f"[경고] 이미지 로딩 실패: {img_path}")
+            continue
+
+        processed = crop_pad_resize(image, target_size=target_size)
+
+        save_path = os.path.join(output_dir, fname)
+        cv2.imwrite(save_path, processed)
diff --git a/ai_train/preprocessing/remap_yolo_labels.py b/ai_train/preprocessing/remap_yolo_labels.py
new file mode 100644
index 000000000..01ac12233
--- /dev/null
+++ b/ai_train/preprocessing/remap_yolo_labels.py
@@ -0,0 +1,81 @@
+# remap_yolo_labels_flexible.py
+import argparse, json, yaml, os, shutil
+from pathlib import Path
+from tqdm import tqdm
+
+IMG_EXT = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
+
+def load_master(master_yaml: Path):
+    with master_yaml.open(encoding="utf-8") as f:
+        names = yaml.safe_load(f)["names"]
+    return {n: i for i, n in enumerate(names)}
+
+def try_subdir(root: Path, split: str, sub: str):
+    """root/train/images → Path or None"""
+    p = root / split / sub
+    return p if p.exists() else None
+
+def main(src_root, dst_root, map_json, master_yaml, hardlink=False):
+    src = Path(src_root)
+    dst = Path(dst_root)
+    dst.mkdir(parents=True, exist_ok=True)
+
+    # --- 사전 로드
+    master2idx = load_master(Path(master_yaml))
+    mapping = json.load(open(map_json, encoding="utf-8"))         # old → {master_name, master_id}
+
+    # 원본 data.yaml이 없어도 되도록 names 리스트를 map_json에서 역추적
+    id2old = list(mapping.keys())                                 # old_id → old_name (가정)
+
+    # --- 새 data.yaml 작성
+    yaml.safe_dump({
+        "path": str(dst.resolve()),
+        "train": "train/images",
+        "val":   "val/images",
+        "test":  "test/images",
+        "nc": len(master2idx),
+        "names": list(master2idx.keys())
+    }, open(dst/"data.yaml", "w", encoding="utf-8"), allow_unicode=True)
+
+    # --- 존재하는 split만 순회
+    for split in ["train", "val", "test"]:
+        img_src = try_subdir(src, split, "images")
+        lbl_src = try_subdir(src, split, "labels")
+        if not img_src:                 # 이 split은 없다는 뜻
+            continue
+
+        # 대상 폴더 생성
+        (dst/split/"images").mkdir(parents=True, exist_ok=True)
+        (dst/split/"labels").mkdir(parents=True, exist_ok=True)
+
+        for img_path in tqdm(img_src.iterdir(), desc=f"{split}"):
+            if img_path.suffix.lower() not in IMG_EXT:
+                continue
+
+            # 1) 이미지 복사/링크
+            target_img = dst/split/"images"/img_path.name
+            if hardlink:
+                os.link(img_path, target_img)
+            else:
+                shutil.copy2(img_path, target_img)
+
+            # 2) 라벨 변환
+            if lbl_src:
+                label_txt = lbl_src/(img_path.stem + ".txt")
+                if label_txt.exists():
+                    new_lines = []
+                    for ln in label_txt.read_text().splitlines():
+                        old_id, *rest = ln.split()
+                        old_name   = id2old[int(old_id)]
+                        master_id  = mapping[old_name]["master_id"]
+                        new_lines.append(" ".join([str(master_id)] + rest))
+                    (dst/split/"labels"/label_txt.name).write_text("\n".join(new_lines))
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--src_root", required=True)
+    ap.add_argument("--dst_root", required=True)
+    ap.add_argument("--map_json", required=True)
+    ap.add_argument("--master_yaml", required=True)
+    ap.add_argument("--hardlink", action="store_true")
+    main(**vars(ap.parse_args()))
diff --git a/ai_train/preprocessing/symbol_crop_count.py b/ai_train/preprocessing/symbol_crop_count.py
new file mode 100644
index 000000000..618ddab5b
--- /dev/null
+++ b/ai_train/preprocessing/symbol_crop_count.py
@@ -0,0 +1,36 @@
+import os
+import matplotlib.pyplot as plt
+
+# 데이터셋 경로 설정 (윈도우 경로)
+dataset_path = "C:\LAB\python\original_washtag_dataset"
+
+# 클래스 번호와 카운트를 저장할 리스트 초기화
+class_labels = [str(i) for i in range(43)]
+class_counts = []
+
+# 각 클래스 디렉토리(0~42) 내 파일 개수 계산
+for i in range(43):
+    class_dir = os.path.join(dataset_path, str(i))
+    if os.path.isdir(class_dir):
+        files = [f for f in os.listdir(class_dir) 
+                 if os.path.isfile(os.path.join(class_dir, f))]
+        count = len(files)
+    else:
+        count = 0
+    class_counts.append(count)
+
+# 그래프 그리기
+plt.figure(figsize=(12, 6))
+plt.bar(class_labels, class_counts)
+plt.xlabel("Class")
+plt.ylabel("Number of images")
+plt.title("Number of images per class")
+plt.xticks(rotation=90)
+plt.tight_layout()
+
+# 저장 및 출력
+output_path = "class_distribution.png"
+plt.savefig(output_path)
+plt.show()
+
+print(f"Saved bar chart to {output_path}")
diff --git a/ai_train/preprocessing/visualize/stain_rgb_dist.py b/ai_train/preprocessing/visualize/stain_rgb_dist.py
new file mode 100644
index 000000000..f1b51ccad
--- /dev/null
+++ b/ai_train/preprocessing/visualize/stain_rgb_dist.py
@@ -0,0 +1,129 @@
+import os
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+
+# ——————————————————————————————————————————————
+# 설정만 바꿔주세요
+DATASET_DIR = '/home/t25119/aiLab/visualization/data/stain_data_crop'   # 예: /home/t25119/aiLab/data/stain, 클래스별 폴더 아래에 images/, labels/ 폴더 존재
+OUTPUT_DIR  = '/home/t25119/aiLab/visualization/output'    # 저장될 그래프 폴더
+CLASSES     = ['blood','coffee','earth','ink','kimchi','lipstick','mustard','oil','wine']
+# ——————————————————————————————————————————————
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# 크롭 영역 불러오기
+# YOLO 포맷 라벨(images가름)/labels 폴더 사용
+
+def load_crops_from_class(cls_name):
+    crops = []
+    img_dir = os.path.join(DATASET_DIR, cls_name, 'images')
+    lbl_dir = os.path.join(DATASET_DIR, cls_name, 'labels')
+    for fname in os.listdir(img_dir):
+        if not fname.lower().endswith(('.png','.jpg','.jpeg')):
+            continue
+        img_path = os.path.join(img_dir, fname)
+        lbl_path = os.path.join(lbl_dir, os.path.splitext(fname)[0] + '.txt')
+        try:
+            img = Image.open(img_path).convert('RGB')
+        except:
+            continue
+        w, h = img.size
+        if not os.path.exists(lbl_path):
+            continue
+        with open(lbl_path) as f:
+            for line in f:
+                parts = line.strip().split()
+                if len(parts) < 5:
+                    continue
+                # class_id 필요한 경우 parts[0]
+                _, xc, yc, bw, bh = map(float, parts)
+                x1 = int((xc - bw/2) * w)
+                y1 = int((yc - bh/2) * h)
+                x2 = int((xc + bw/2) * w)
+                y2 = int((yc + bh/2) * h)
+                crop = img.crop((x1, y1, x2, y2))
+                if crop.width == 0 or crop.height == 0:
+                    continue
+                crops.append(np.array(crop))
+    return crops
+
+# 클래스별 RGB 히스토그램
+
+def plot_rgb_histograms(crops, cls_name):
+    pixels = np.concatenate([crop.reshape(-1,3) for crop in crops], axis=0)
+    colors = ('r','g','b')
+    plt.figure(figsize=(6,4))
+    for i, c in enumerate(colors):
+        plt.hist(pixels[:,i], bins=256, color=c, alpha=0.6, label=f'{c.upper()} channel')
+    plt.title(f'{cls_name} RGB Histogram (Crops)')
+    plt.xlabel('Pixel intensity')
+    plt.ylabel('Count')
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(OUTPUT_DIR, f'{cls_name}_rgb_hist.png'))
+    plt.close()
+
+# 평균 색상 막대그래프
+
+def plot_mean_color_bar(mean_colors, classes):
+    plt.figure(figsize=(8,2))
+    positions = np.arange(len(classes))
+    colors = mean_colors / 255.0
+    plt.bar(positions, [1]*len(classes), color=colors, edgecolor='k')
+    plt.xticks(positions, classes, rotation=45, ha='right')
+    plt.yticks([])
+    plt.title('Class-wise Mean Color (Crops)')
+    plt.tight_layout()
+    plt.savefig(os.path.join(OUTPUT_DIR, 'mean_color_bar.png'))
+    plt.close()
+
+# 클래스 분포
+
+def plot_class_distribution(counts, classes):
+    plt.figure(figsize=(6,4))
+    plt.bar(classes, counts, color='skyblue', edgecolor='k')
+    plt.xticks(rotation=45, ha='right')
+    plt.ylabel('Number of crops')
+    plt.title('Crop Count per Class')
+    plt.tight_layout()
+    plt.savefig(os.path.join(OUTPUT_DIR, 'class_distribution.png'))
+    plt.close()
+
+# 평균 밝기 히스토그램
+
+def plot_mean_brightness_hist(crops, cls_name):
+    pixels = np.concatenate([crop.reshape(-1,3) for crop in crops], axis=0)
+    brightness = pixels.mean(axis=1)
+    plt.figure(figsize=(6,4))
+    plt.hist(brightness, bins=256, color='gray', alpha=0.7)
+    plt.title(f'{cls_name} Brightness Histogram (Crops)')
+    plt.xlabel('Brightness')
+    plt.ylabel('Count')
+    plt.tight_layout()
+    plt.savefig(os.path.join(OUTPUT_DIR, f'{cls_name}_brightness_hist.png'))
+    plt.close()
+
+# 메인 함수
+
+def main():
+    counts = []
+    mean_colors = []
+    for cls in CLASSES:
+        print(f'Processing class: {cls}')
+        crops = load_crops_from_class(cls)
+        counts.append(len(crops))
+        if not crops:
+            continue
+        plot_rgb_histograms(crops, cls)
+        # 이미지별 평균 색상 계산 후 클래스 평균
+        img_means = np.vstack([crop.reshape(-1,3).mean(axis=0) for crop in crops])
+        class_mean = img_means.mean(axis=0)
+        mean_colors.append(class_mean)
+        plot_mean_brightness_hist(crops, cls)
+    mean_colors = np.array(mean_colors)
+    plot_class_distribution(counts, CLASSES)
+    plot_mean_color_bar(mean_colors, CLASSES)
+
+if __name__ == '__main__':
+    main()
diff --git a/ai_train/preprocessing/visualize/stain_tnse.py b/ai_train/preprocessing/visualize/stain_tnse.py
new file mode 100644
index 000000000..d5d3b4c19
--- /dev/null
+++ b/ai_train/preprocessing/visualize/stain_tnse.py
@@ -0,0 +1,141 @@
+import os
+import random
+import numpy as np
+import pandas as pd
+from PIL import Image
+#from skimage.feature import hog
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+
+# ——————————————————————————————————————————————
+# 설정
+DATASET_DIR      = '/home/t25119/aiLab/visualization/data/stain_data_crop'       # 예시: /home/t25119/aiLab/data/stain/test
+OUTPUT_DIR       = '/home/t25119/aiLab/visualization/output'        # 그래프 저장 경로
+CLASSES          = ['blood', 'coffee', 'earth', 'ink', 'kimchi', 'lipstick', 'mustard', 'oil', 'wine']
+PIXELS_PER_CLASS = 5000                     # t-SNE 샘플 픽셀 수/클래스
+# ——————————————————————————————————————————————
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# 1) 클래스별 크롭된 얼룩 영역 불러오기
+def load_crops(cls):
+    crops = []
+    img_dir = os.path.join(DATASET_DIR, cls, 'images')
+    lbl_dir = os.path.join(DATASET_DIR, cls, 'labels')
+    for fname in os.listdir(img_dir):
+        if not fname.lower().endswith(('.png', '.jpg', '.jpeg')):
+            continue
+        img_path = os.path.join(img_dir, fname)
+        lbl_path = os.path.join(lbl_dir, os.path.splitext(fname)[0] + '.txt')
+        try:
+            img = Image.open(img_path).convert('RGB')
+        except:
+            continue
+        w, h = img.size
+        # 레이블 파일을 읽어, bbox 단위로 크롭
+        if not os.path.exists(lbl_path):
+            continue
+        with open(lbl_path) as f:
+            for line in f:
+                parts = line.strip().split()
+                if len(parts) < 5:
+                    continue
+                # YOLO 포맷: class_id x_center y_center width height (정규화)
+                _, xc, yc, bw, bh = map(float, parts)
+                x1 = int((xc - bw/2) * w)
+                y1 = int((yc - bh/2) * h)
+                x2 = int((xc + bw/2) * w)
+                y2 = int((yc + bh/2) * h)
+                crop = np.array(img.crop((x1, y1, x2, y2)))
+                if crop.size == 0:
+                    continue
+                crops.append(crop)
+    return crops
+
+# 2) 클래스별 평균 RGB 막대그래프
+def plot_class_mean_rgb(mean_colors):
+    x = np.arange(len(CLASSES))
+    width = 0.25
+    fig, ax = plt.subplots(figsize=(8, 4))
+    for i, col in enumerate(['R', 'G', 'B']):
+        ax.bar(x + i * width, mean_colors[:, i], width, label=col)
+    ax.set_xticks(x + width)
+    ax.set_xticklabels(CLASSES, rotation=45, ha='right')
+    ax.set_ylabel('Mean Intensity')
+    ax.set_title('Class-wise Mean RGB (Cropped Regions)')
+    ax.legend()
+    fig.tight_layout()
+    fig.savefig(os.path.join(OUTPUT_DIR, 'class_mean_rgb.png'))
+    plt.close(fig)
+
+# 3) t-SNE 기반 RGB 분포 시각화
+def plot_rgb_tsne():
+    all_pixels = []
+    labels = []
+    for idx, cls in enumerate(CLASSES):
+        crops = load_crops(cls)
+        pixels = np.concatenate([crop.reshape(-1, 3) for crop in crops], axis=0)
+        sel = pixels[np.random.choice(len(pixels), PIXELS_PER_CLASS, replace=False)]
+        all_pixels.append(sel)
+        labels += [cls] * PIXELS_PER_CLASS
+    data = np.vstack(all_pixels)
+    tsne = TSNE(n_components=2, random_state=0)
+    proj = tsne.fit_transform(data.astype(np.float32))
+    plt.figure(figsize=(6, 6))
+    for cls in CLASSES:
+        idxs = [i for i, l in enumerate(labels) if l == cls]
+        plt.scatter(proj[idxs, 0], proj[idxs, 1], s=2, label=cls, alpha=0.6)
+    plt.legend(markerscale=4, fontsize='small')
+    plt.title('t-SNE on RGB Pixels (Cropped Regions)')
+    plt.tight_layout()
+    plt.savefig(os.path.join(OUTPUT_DIR, 'rgb_pixels_tsne.png'))
+    plt.close()
+
+# 4) 통합 피처 기반 t-SNE 시각화
+def plot_integrated_tsne():
+    feats = []
+    labs = []
+    for cls in CLASSES:
+        crops = load_crops(cls)
+        for crop in crops:
+            h, w = crop.shape[:2]
+            # 1) mean RGB
+            mean_rgb = crop.reshape(-1, 3).mean(axis=0)
+            # 2) HOG (grayscale placeholder)
+            gray = np.dot(crop[..., :3], [0.2989, 0.5870, 0.1140])
+            # hog_feat = hog(gray, pixels_per_cell=(32, 32), cells_per_block=(2, 2), feature_vector=True)
+            hog_feat = np.zeros(1)
+            # 3) 위치/비율 기본값(중앙, 종횡비 1)
+            cx, cy, ar = 0.5, 0.5, 1.0
+            feats.append(np.hstack([mean_rgb, hog_feat, cx, cy, ar]))
+            labs.append(cls)
+    X = np.vstack(feats).astype(np.float32)
+    tsne = TSNE(n_components=2, random_state=0)
+    proj = tsne.fit_transform(X)
+    plt.figure(figsize=(6, 6))
+    for cls in CLASSES:
+        idxs = [i for i, l in enumerate(labs) if l == cls]
+        plt.scatter(proj[idxs, 0], proj[idxs, 1], s=5, label=cls, alpha=0.7)
+    plt.legend(markerscale=4, fontsize='small')
+    plt.title('t-SNE on Integrated Features (Cropped Regions)')
+    plt.tight_layout()
+    plt.savefig(os.path.join(OUTPUT_DIR, 'integrated_tsne.png'))
+    plt.close()
+
+
+def main():
+    mean_colors = []
+    for cls in CLASSES:
+        crops = load_crops(cls)
+        if not crops:
+            continue
+        class_mean = np.vstack([crop.reshape(-1, 3).mean(axis=0) for crop in crops]).mean(axis=0)
+        mean_colors.append(class_mean)
+    mean_colors = np.array(mean_colors)
+
+    plot_class_mean_rgb(mean_colors)
+    plot_rgb_tsne()
+    plot_integrated_tsne()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
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+++ b/ai_train/retrain/data/stain/data.yaml
@@ -0,0 +1,6 @@
+train: /home/t25119/aiLab/test/rushWash/ai/data/stain/train/images
+val: /home/t25119/aiLab/test/rushWash/ai/data/stain/val/images
+test: /home/t25119/aiLab/test/rushWash/ai/data/stain/test/images
+
+nc: 9
+names: [blood, coffee, earth, ink, kimchi, lipstick, mustard, oil, wine]
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+0 0.240000 0.284153 0.043636 0.032787
diff --git a/ai_train/retrain/data/stain/val/labels/blood_029.txt b/ai_train/retrain/data/stain/val/labels/blood_029.txt
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+0 0.343511 0.489583 0.244275 0.281250
+0 0.656489 0.382812 0.282443 0.348958
diff --git a/ai_train/retrain/data/stain/val/labels/blood_034.txt b/ai_train/retrain/data/stain/val/labels/blood_034.txt
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index 000000000..2a1bae876
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_036.txt b/ai_train/retrain/data/stain/val/labels/blood_036.txt
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index 000000000..49351c4b9
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+0 0.637405 0.326425 0.274809 0.279793
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index 000000000..6c46c4d4f
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_050.txt b/ai_train/retrain/data/stain/val/labels/blood_050.txt
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index 000000000..3fb369156
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+0 0.495000 0.351190 0.120000 0.075397
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_052.txt b/ai_train/retrain/data/stain/val/labels/blood_052.txt
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index 000000000..5d0249ee0
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+++ b/ai_train/retrain/data/stain/val/labels/blood_052.txt
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+0 0.498333 0.577381 0.123333 0.250000
diff --git a/ai_train/retrain/data/stain/val/labels/blood_061.txt b/ai_train/retrain/data/stain/val/labels/blood_061.txt
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index 000000000..594747de8
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+++ b/ai_train/retrain/data/stain/val/labels/blood_061.txt
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+0 0.450909 0.486339 0.727273 0.655738
diff --git a/ai_train/retrain/data/stain/val/labels/blood_069.txt b/ai_train/retrain/data/stain/val/labels/blood_069.txt
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index 000000000..338999d9c
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+++ b/ai_train/retrain/data/stain/val/labels/blood_069.txt
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+0 0.477178 0.413876 0.091286 0.081340
diff --git a/ai_train/retrain/data/stain/val/labels/blood_071.txt b/ai_train/retrain/data/stain/val/labels/blood_071.txt
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index 000000000..96e068191
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_071.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_075.txt b/ai_train/retrain/data/stain/val/labels/blood_075.txt
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index 000000000..098e8feab
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_075.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_077.txt b/ai_train/retrain/data/stain/val/labels/blood_077.txt
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index 000000000..6488a6fc3
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_077.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_084.txt b/ai_train/retrain/data/stain/val/labels/blood_084.txt
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index 000000000..4df49b89f
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_084.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_085.txt b/ai_train/retrain/data/stain/val/labels/blood_085.txt
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index 000000000..358c2cd15
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_085.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_093.txt b/ai_train/retrain/data/stain/val/labels/blood_093.txt
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index 000000000..cc5368f41
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_093.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_097.txt b/ai_train/retrain/data/stain/val/labels/blood_097.txt
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index 000000000..b083d6e76
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_097.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/blood_099.txt b/ai_train/retrain/data/stain/val/labels/blood_099.txt
new file mode 100644
index 000000000..649dc6f05
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/blood_099.txt
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diff --git a/ai_train/retrain/data/stain/val/labels/coffee_001.txt b/ai_train/retrain/data/stain/val/labels/coffee_001.txt
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index 000000000..b7acbad9c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_001.txt
@@ -0,0 +1 @@
+1 0.282609 0.538690 0.150502 0.363095
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_005.txt b/ai_train/retrain/data/stain/val/labels/coffee_005.txt
new file mode 100644
index 000000000..bd542fb86
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_005.txt
@@ -0,0 +1 @@
+1 0.195423 0.646893 0.214789 0.310734
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_010.txt b/ai_train/retrain/data/stain/val/labels/coffee_010.txt
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index 000000000..2c9acdbf8
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_010.txt
@@ -0,0 +1 @@
+1 0.669091 0.532787 0.254545 0.879781
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_011.txt b/ai_train/retrain/data/stain/val/labels/coffee_011.txt
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index 000000000..d059c24b8
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_011.txt
@@ -0,0 +1 @@
+1 0.554054 0.365639 0.108108 0.123348
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_019.txt b/ai_train/retrain/data/stain/val/labels/coffee_019.txt
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index 000000000..552b7a320
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_019.txt
@@ -0,0 +1 @@
+1 0.091525 0.163743 0.074576 0.233918
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_021.txt b/ai_train/retrain/data/stain/val/labels/coffee_021.txt
new file mode 100644
index 000000000..dbb113b95
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_021.txt
@@ -0,0 +1 @@
+1 0.330909 0.215847 0.065455 0.213115
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_024.txt b/ai_train/retrain/data/stain/val/labels/coffee_024.txt
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index 000000000..1fbb1d488
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_024.txt
@@ -0,0 +1,2 @@
+1 0.543750 0.631250 0.062500 0.100000
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diff --git a/ai_train/retrain/data/stain/val/labels/coffee_035.txt b/ai_train/retrain/data/stain/val/labels/coffee_035.txt
new file mode 100644
index 000000000..33a32f482
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_035.txt
@@ -0,0 +1,4 @@
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diff --git a/ai_train/retrain/data/stain/val/labels/coffee_050.txt b/ai_train/retrain/data/stain/val/labels/coffee_050.txt
new file mode 100644
index 000000000..fc23e5c9b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_050.txt
@@ -0,0 +1,2 @@
+1 0.632212 0.645433 0.274038 0.242788
+1 0.758413 0.456731 0.209135 0.105769
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_051.txt b/ai_train/retrain/data/stain/val/labels/coffee_051.txt
new file mode 100644
index 000000000..bc4e596b8
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_051.txt
@@ -0,0 +1,2 @@
+1 0.840144 0.754808 0.185096 0.134615
+1 0.804087 0.890625 0.112981 0.112981
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_055.txt b/ai_train/retrain/data/stain/val/labels/coffee_055.txt
new file mode 100644
index 000000000..ced5e1f09
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_055.txt
@@ -0,0 +1 @@
+1 0.768029 0.370192 0.079327 0.125000
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_067.txt b/ai_train/retrain/data/stain/val/labels/coffee_067.txt
new file mode 100644
index 000000000..7bb3cce1a
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_067.txt
@@ -0,0 +1,3 @@
+1 0.753606 0.418269 0.218750 0.115385
+1 0.635817 0.313702 0.060096 0.074519
+1 0.721154 0.520433 0.100962 0.064904
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_070.txt b/ai_train/retrain/data/stain/val/labels/coffee_070.txt
new file mode 100644
index 000000000..1552e3645
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_070.txt
@@ -0,0 +1,3 @@
+1 0.475962 0.378606 0.144231 0.156250
+1 0.508413 0.593750 0.151442 0.129808
+1 0.461538 0.813702 0.100962 0.084135
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_072.txt b/ai_train/retrain/data/stain/val/labels/coffee_072.txt
new file mode 100644
index 000000000..545b7370e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_072.txt
@@ -0,0 +1 @@
+1 0.651442 0.718750 0.100962 0.091346
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_075.txt b/ai_train/retrain/data/stain/val/labels/coffee_075.txt
new file mode 100644
index 000000000..cca24f65b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_075.txt
@@ -0,0 +1,7 @@
+1 0.558894 0.510817 0.040865 0.069712
+1 0.622596 0.389423 0.048077 0.163462
+1 0.545673 0.342548 0.024038 0.026442
+1 0.362981 0.355769 0.038462 0.024038
+1 0.324519 0.554087 0.024038 0.031250
+1 0.325721 0.685096 0.026442 0.028846
+1 0.388221 0.259615 0.060096 0.052885
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_078.txt b/ai_train/retrain/data/stain/val/labels/coffee_078.txt
new file mode 100644
index 000000000..58c4a26cc
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_078.txt
@@ -0,0 +1 @@
+1 0.656250 0.395433 0.182692 0.194712
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_079.txt b/ai_train/retrain/data/stain/val/labels/coffee_079.txt
new file mode 100644
index 000000000..263f8bb94
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_079.txt
@@ -0,0 +1,2 @@
+1 0.602163 0.290865 0.084135 0.158654
+1 0.480769 0.342548 0.057692 0.112981
diff --git a/ai_train/retrain/data/stain/val/labels/coffee_087.txt b/ai_train/retrain/data/stain/val/labels/coffee_087.txt
new file mode 100644
index 000000000..e9bee8d65
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/coffee_087.txt
@@ -0,0 +1,2 @@
+1 0.580529 0.772837 0.262019 0.132212
+1 0.912260 0.544471 0.098558 0.074519
diff --git a/ai_train/retrain/data/stain/val/labels/earth_109.txt b/ai_train/retrain/data/stain/val/labels/earth_109.txt
new file mode 100644
index 000000000..8d7300553
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_109.txt
@@ -0,0 +1,4 @@
+2 0.408879 0.625000 0.079439 0.131356
+2 0.621495 0.622881 0.074766 0.127119
+2 0.338785 0.489407 0.070093 0.122881
+2 0.738318 0.512712 0.037383 0.076271
diff --git a/ai_train/retrain/data/stain/val/labels/earth_115.txt b/ai_train/retrain/data/stain/val/labels/earth_115.txt
new file mode 100644
index 000000000..0875b40bb
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_115.txt
@@ -0,0 +1,4 @@
+2 0.708763 0.291506 0.139175 0.096525
+2 0.884021 0.418919 0.097938 0.166023
+2 0.595361 0.555985 0.108247 0.077220
+2 0.530928 0.461390 0.061856 0.073359
diff --git a/ai_train/retrain/data/stain/val/labels/earth_125.txt b/ai_train/retrain/data/stain/val/labels/earth_125.txt
new file mode 100644
index 000000000..0337a902d
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_125.txt
@@ -0,0 +1,2 @@
+2 0.427273 0.513661 0.301818 0.218579
+2 0.343636 0.677596 0.105455 0.087432
diff --git a/ai_train/retrain/data/stain/val/labels/earth_127.txt b/ai_train/retrain/data/stain/val/labels/earth_127.txt
new file mode 100644
index 000000000..5ef9c0b95
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_127.txt
@@ -0,0 +1,3 @@
+2 0.429260 0.543210 0.073955 0.308642
+2 0.665595 0.614198 0.070740 0.277778
+2 0.350482 0.509259 0.025723 0.104938
diff --git a/ai_train/retrain/data/stain/val/labels/earth_128.txt b/ai_train/retrain/data/stain/val/labels/earth_128.txt
new file mode 100644
index 000000000..195e82a91
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_128.txt
@@ -0,0 +1,2 @@
+2 0.738095 0.545000 0.095238 0.083333
+2 0.279762 0.523333 0.083333 0.080000
diff --git a/ai_train/retrain/data/stain/val/labels/earth_130.txt b/ai_train/retrain/data/stain/val/labels/earth_130.txt
new file mode 100644
index 000000000..ddb7a6805
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_130.txt
@@ -0,0 +1,2 @@
+2 0.240000 0.593333 0.222222 0.191111
+2 0.777778 0.480000 0.231111 0.364444
diff --git a/ai_train/retrain/data/stain/val/labels/earth_133.txt b/ai_train/retrain/data/stain/val/labels/earth_133.txt
new file mode 100644
index 000000000..208f9a658
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_133.txt
@@ -0,0 +1,7 @@
+2 0.536260 0.791667 0.103053 0.156250
+2 0.509542 0.914062 0.087786 0.067708
+2 0.698473 0.533854 0.083969 0.109375
+2 0.458015 0.778646 0.030534 0.067708
+2 0.425573 0.830729 0.034351 0.046875
+2 0.633588 0.979167 0.045802 0.041667
+2 0.204198 0.583333 0.072519 0.062500
diff --git a/ai_train/retrain/data/stain/val/labels/earth_134.txt b/ai_train/retrain/data/stain/val/labels/earth_134.txt
new file mode 100644
index 000000000..26e2d99fe
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_134.txt
@@ -0,0 +1,2 @@
+2 0.341818 0.896175 0.116364 0.207650
+2 0.472727 0.945355 0.036364 0.098361
diff --git a/ai_train/retrain/data/stain/val/labels/earth_135.txt b/ai_train/retrain/data/stain/val/labels/earth_135.txt
new file mode 100644
index 000000000..9c66113b5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_135.txt
@@ -0,0 +1,2 @@
+2 0.416667 0.398810 0.073333 0.369048
+2 0.661667 0.473214 0.063333 0.208333
diff --git a/ai_train/retrain/data/stain/val/labels/earth_138.txt b/ai_train/retrain/data/stain/val/labels/earth_138.txt
new file mode 100644
index 000000000..c5c13aab4
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_138.txt
@@ -0,0 +1,5 @@
+2 0.330909 0.057377 0.116364 0.103825
+2 0.532727 0.062842 0.112727 0.114754
+2 0.641818 0.155738 0.069091 0.125683
+2 0.698182 0.584699 0.050909 0.065574
+2 0.214545 0.857923 0.240000 0.218579
diff --git a/ai_train/retrain/data/stain/val/labels/earth_142.txt b/ai_train/retrain/data/stain/val/labels/earth_142.txt
new file mode 100644
index 000000000..8a6e1b4f0
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_142.txt
@@ -0,0 +1 @@
+2 0.505618 0.743386 0.284644 0.386243
diff --git a/ai_train/retrain/data/stain/val/labels/earth_143.txt b/ai_train/retrain/data/stain/val/labels/earth_143.txt
new file mode 100644
index 000000000..25c5dfe25
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_143.txt
@@ -0,0 +1 @@
+2 0.503636 0.538251 0.134545 0.224044
diff --git a/ai_train/retrain/data/stain/val/labels/earth_147.txt b/ai_train/retrain/data/stain/val/labels/earth_147.txt
new file mode 100644
index 000000000..bca96fef3
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_147.txt
@@ -0,0 +1 @@
+2 0.698997 0.205357 0.086957 0.184524
diff --git a/ai_train/retrain/data/stain/val/labels/earth_19.txt b/ai_train/retrain/data/stain/val/labels/earth_19.txt
new file mode 100644
index 000000000..86de398fb
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_19.txt
@@ -0,0 +1 @@
+2 0.488679 0.384211 0.147170 0.284211
diff --git a/ai_train/retrain/data/stain/val/labels/earth_20.txt b/ai_train/retrain/data/stain/val/labels/earth_20.txt
new file mode 100644
index 000000000..84258d80f
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_20.txt
@@ -0,0 +1 @@
+2 0.666023 0.471649 0.088803 0.139175
diff --git a/ai_train/retrain/data/stain/val/labels/earth_25.txt b/ai_train/retrain/data/stain/val/labels/earth_25.txt
new file mode 100644
index 000000000..69b1865d9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_25.txt
@@ -0,0 +1,2 @@
+2 0.478333 0.574405 0.056667 0.148810
+2 0.325000 0.607143 0.070000 0.154762
diff --git a/ai_train/retrain/data/stain/val/labels/earth_36.txt b/ai_train/retrain/data/stain/val/labels/earth_36.txt
new file mode 100644
index 000000000..881d478e9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_36.txt
@@ -0,0 +1,2 @@
+2 0.352830 0.368421 0.056604 0.189474
+2 0.743396 0.534211 0.075472 0.173684
diff --git a/ai_train/retrain/data/stain/val/labels/earth_37.txt b/ai_train/retrain/data/stain/val/labels/earth_37.txt
new file mode 100644
index 000000000..f08cc9982
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_37.txt
@@ -0,0 +1,4 @@
+2 0.366667 0.542411 0.040000 0.049107
+2 0.502222 0.571429 0.017778 0.044643
+2 0.477778 0.616071 0.031111 0.026786
+2 0.542222 0.580357 0.026667 0.026786
diff --git a/ai_train/retrain/data/stain/val/labels/earth_38.txt b/ai_train/retrain/data/stain/val/labels/earth_38.txt
new file mode 100644
index 000000000..c76db3c4c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_38.txt
@@ -0,0 +1,6 @@
+2 0.183636 0.357923 0.156364 0.224044
+2 0.563636 0.871585 0.320000 0.256831
+2 0.925455 0.267760 0.120000 0.196721
+2 0.785455 0.442623 0.072727 0.262295
+2 0.660000 0.065574 0.105455 0.120219
+2 0.534545 0.267760 0.174545 0.174863
diff --git a/ai_train/retrain/data/stain/val/labels/earth_4.txt b/ai_train/retrain/data/stain/val/labels/earth_4.txt
new file mode 100644
index 000000000..ca2ce4ad8
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_4.txt
@@ -0,0 +1,2 @@
+2 0.670968 0.496933 0.103226 0.134969
+2 0.874194 0.582822 0.083871 0.171779
diff --git a/ai_train/retrain/data/stain/val/labels/earth_40.txt b/ai_train/retrain/data/stain/val/labels/earth_40.txt
new file mode 100644
index 000000000..1097165a5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_40.txt
@@ -0,0 +1,2 @@
+2 0.590909 0.379781 0.069091 0.125683
+2 0.101818 0.636612 0.065455 0.158470
diff --git a/ai_train/retrain/data/stain/val/labels/earth_42.txt b/ai_train/retrain/data/stain/val/labels/earth_42.txt
new file mode 100644
index 000000000..8f2836417
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_42.txt
@@ -0,0 +1 @@
+2 0.503448 0.511494 0.131034 0.310345
diff --git a/ai_train/retrain/data/stain/val/labels/earth_47.txt b/ai_train/retrain/data/stain/val/labels/earth_47.txt
new file mode 100644
index 000000000..77d4010db
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_47.txt
@@ -0,0 +1,2 @@
+2 0.420755 0.194737 0.056604 0.126316
+2 0.773585 0.155263 0.045283 0.100000
diff --git a/ai_train/retrain/data/stain/val/labels/earth_5.txt b/ai_train/retrain/data/stain/val/labels/earth_5.txt
new file mode 100644
index 000000000..f9ec9be66
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_5.txt
@@ -0,0 +1 @@
+2 0.604247 0.530928 0.212355 0.309278
diff --git a/ai_train/retrain/data/stain/val/labels/earth_60.txt b/ai_train/retrain/data/stain/val/labels/earth_60.txt
new file mode 100644
index 000000000..8764c8b44
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_60.txt
@@ -0,0 +1,2 @@
+2 0.505000 0.544643 0.063333 0.101190
+2 0.400000 0.491071 0.046667 0.065476
diff --git a/ai_train/retrain/data/stain/val/labels/earth_64.txt b/ai_train/retrain/data/stain/val/labels/earth_64.txt
new file mode 100644
index 000000000..a0ec2a450
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_64.txt
@@ -0,0 +1,2 @@
+2 0.200000 0.666667 0.046667 0.107143
+2 0.770000 0.836310 0.073333 0.136905
diff --git a/ai_train/retrain/data/stain/val/labels/earth_84.txt b/ai_train/retrain/data/stain/val/labels/earth_84.txt
new file mode 100644
index 000000000..db0d0ba4e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_84.txt
@@ -0,0 +1 @@
+2 0.518333 0.422619 0.223333 0.226190
diff --git a/ai_train/retrain/data/stain/val/labels/earth_85.txt b/ai_train/retrain/data/stain/val/labels/earth_85.txt
new file mode 100644
index 000000000..9a570f3f5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_85.txt
@@ -0,0 +1,5 @@
+2 0.271795 0.488372 0.112821 0.116279
+2 0.258974 0.116279 0.117949 0.062016
+2 0.612821 0.255814 0.087179 0.062016
+2 0.671795 0.449612 0.102564 0.054264
+2 0.266667 0.286822 0.071795 0.077519
diff --git a/ai_train/retrain/data/stain/val/labels/earth_93.txt b/ai_train/retrain/data/stain/val/labels/earth_93.txt
new file mode 100644
index 000000000..80762832b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/earth_93.txt
@@ -0,0 +1,3 @@
+2 0.656364 0.551913 0.207273 0.327869
+2 0.481818 0.625683 0.112727 0.333333
+2 0.358182 0.658470 0.105455 0.147541
diff --git a/ai_train/retrain/data/stain/val/labels/ink_008.txt b/ai_train/retrain/data/stain/val/labels/ink_008.txt
new file mode 100644
index 000000000..836dbe9f5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_008.txt
@@ -0,0 +1 @@
+3 0.514706 0.612903 0.080882 0.182796
diff --git a/ai_train/retrain/data/stain/val/labels/ink_013.txt b/ai_train/retrain/data/stain/val/labels/ink_013.txt
new file mode 100644
index 000000000..5b2627cc1
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_013.txt
@@ -0,0 +1 @@
+3 0.285455 0.352459 0.083636 0.092896
diff --git a/ai_train/retrain/data/stain/val/labels/ink_018.txt b/ai_train/retrain/data/stain/val/labels/ink_018.txt
new file mode 100644
index 000000000..e3ada1850
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_018.txt
@@ -0,0 +1 @@
+3 0.748188 0.434426 0.228261 0.278689
diff --git a/ai_train/retrain/data/stain/val/labels/ink_033.txt b/ai_train/retrain/data/stain/val/labels/ink_033.txt
new file mode 100644
index 000000000..d96b1564f
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_033.txt
@@ -0,0 +1 @@
+3 0.478448 0.344828 0.071839 0.151724
diff --git a/ai_train/retrain/data/stain/val/labels/ink_037.txt b/ai_train/retrain/data/stain/val/labels/ink_037.txt
new file mode 100644
index 000000000..0e35d4e02
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_037.txt
@@ -0,0 +1 @@
+3 0.662116 0.462209 0.129693 0.156977
diff --git a/ai_train/retrain/data/stain/val/labels/ink_041.txt b/ai_train/retrain/data/stain/val/labels/ink_041.txt
new file mode 100644
index 000000000..1ada053a6
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_041.txt
@@ -0,0 +1 @@
+3 0.721992 0.461722 0.024896 0.033493
diff --git a/ai_train/retrain/data/stain/val/labels/ink_044.txt b/ai_train/retrain/data/stain/val/labels/ink_044.txt
new file mode 100644
index 000000000..8496d8d53
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_044.txt
@@ -0,0 +1 @@
+3 0.464883 0.824405 0.073579 0.184524
diff --git a/ai_train/retrain/data/stain/val/labels/ink_052.txt b/ai_train/retrain/data/stain/val/labels/ink_052.txt
new file mode 100644
index 000000000..2658073e7
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_052.txt
@@ -0,0 +1 @@
+3 0.482692 0.837629 0.157692 0.077320
diff --git a/ai_train/retrain/data/stain/val/labels/ink_061.txt b/ai_train/retrain/data/stain/val/labels/ink_061.txt
new file mode 100644
index 000000000..0853a3570
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_061.txt
@@ -0,0 +1 @@
+3 0.636364 0.631148 0.210909 0.311475
diff --git a/ai_train/retrain/data/stain/val/labels/ink_062.txt b/ai_train/retrain/data/stain/val/labels/ink_062.txt
new file mode 100644
index 000000000..22cf2f2a9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_062.txt
@@ -0,0 +1 @@
+3 0.460000 0.464481 0.120000 0.131148
diff --git a/ai_train/retrain/data/stain/val/labels/ink_063.txt b/ai_train/retrain/data/stain/val/labels/ink_063.txt
new file mode 100644
index 000000000..74c6a05fc
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_063.txt
@@ -0,0 +1 @@
+3 0.742222 0.828889 0.133333 0.128889
diff --git a/ai_train/retrain/data/stain/val/labels/ink_065.txt b/ai_train/retrain/data/stain/val/labels/ink_065.txt
new file mode 100644
index 000000000..da663a9ed
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_065.txt
@@ -0,0 +1 @@
+3 0.529963 0.441489 0.123596 0.138298
diff --git a/ai_train/retrain/data/stain/val/labels/ink_069.txt b/ai_train/retrain/data/stain/val/labels/ink_069.txt
new file mode 100644
index 000000000..3379b646b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_069.txt
@@ -0,0 +1 @@
+3 0.490909 0.546448 0.058182 0.076503
diff --git a/ai_train/retrain/data/stain/val/labels/ink_083.txt b/ai_train/retrain/data/stain/val/labels/ink_083.txt
new file mode 100644
index 000000000..105b3ce09
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_083.txt
@@ -0,0 +1,2 @@
+3 0.610825 0.447876 0.149485 0.092664
+3 0.590206 0.577220 0.149485 0.142857
diff --git a/ai_train/retrain/data/stain/val/labels/ink_086.txt b/ai_train/retrain/data/stain/val/labels/ink_086.txt
new file mode 100644
index 000000000..22d222b8b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_086.txt
@@ -0,0 +1,7 @@
+3 0.536082 0.362934 0.061856 0.038610
+3 0.603093 0.416988 0.041237 0.030888
+3 0.592784 0.619691 0.092784 0.111969
+3 0.530928 0.779923 0.051546 0.038610
+3 0.515464 0.861004 0.041237 0.054054
+3 0.414948 0.291506 0.077320 0.027027
+3 0.559278 0.293436 0.046392 0.030888
diff --git a/ai_train/retrain/data/stain/val/labels/ink_088.txt b/ai_train/retrain/data/stain/val/labels/ink_088.txt
new file mode 100644
index 000000000..639eb16d9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/ink_088.txt
@@ -0,0 +1,3 @@
+3 0.567010 0.438224 0.041237 0.034749
+3 0.551546 0.625483 0.061856 0.038610
+3 0.525773 0.777992 0.103093 0.065637
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_003.txt b/ai_train/retrain/data/stain/val/labels/kimchi_003.txt
new file mode 100644
index 000000000..441b53d2e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_003.txt
@@ -0,0 +1,9 @@
+4 0.216406 0.124219 0.032813 0.032813
+4 0.289062 0.312500 0.028125 0.021875
+4 0.521094 0.207813 0.026562 0.028125
+4 0.521875 0.287500 0.028125 0.034375
+4 0.648438 0.393750 0.059375 0.065625
+4 0.614062 0.489844 0.100000 0.092188
+4 0.328906 0.643750 0.307812 0.312500
+4 0.259375 0.886719 0.056250 0.035937
+4 0.362500 0.872656 0.062500 0.042188
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_009.txt b/ai_train/retrain/data/stain/val/labels/kimchi_009.txt
new file mode 100644
index 000000000..b75d0a4af
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_009.txt
@@ -0,0 +1,2 @@
+4 0.451562 0.432031 0.262500 0.242188
+4 0.514062 0.615625 0.106250 0.078125
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_013.txt b/ai_train/retrain/data/stain/val/labels/kimchi_013.txt
new file mode 100644
index 000000000..c255e374e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_013.txt
@@ -0,0 +1,4 @@
+4 0.417969 0.293750 0.129688 0.090625
+4 0.463281 0.430469 0.204687 0.151562
+4 0.268750 0.419531 0.125000 0.064062
+4 0.701562 0.614062 0.056250 0.062500
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_033.txt b/ai_train/retrain/data/stain/val/labels/kimchi_033.txt
new file mode 100644
index 000000000..eaca11d51
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_033.txt
@@ -0,0 +1,9 @@
+4 0.442308 0.360577 0.091346 0.076923
+4 0.588942 0.451923 0.115385 0.139423
+4 0.599760 0.562500 0.050481 0.052885
+4 0.467548 0.848558 0.156250 0.139423
+4 0.284856 0.843750 0.055288 0.043269
+4 0.443510 0.524038 0.170673 0.168269
+4 0.760817 0.500000 0.079327 0.062500
+4 0.731971 0.552885 0.117788 0.062500
+4 0.689904 0.604567 0.024038 0.026442
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_038.txt b/ai_train/retrain/data/stain/val/labels/kimchi_038.txt
new file mode 100644
index 000000000..eea2b27a7
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_038.txt
@@ -0,0 +1,3 @@
+4 0.331731 0.483173 0.062500 0.076923
+4 0.322115 0.600962 0.038462 0.043269
+4 0.631010 0.677885 0.223558 0.346154
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_041.txt b/ai_train/retrain/data/stain/val/labels/kimchi_041.txt
new file mode 100644
index 000000000..20aa182f6
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_041.txt
@@ -0,0 +1,7 @@
+4 0.632212 0.352163 0.052885 0.040865
+4 0.551683 0.408654 0.050481 0.043269
+4 0.460337 0.485577 0.098558 0.115385
+4 0.629808 0.562500 0.096154 0.076923
+4 0.861779 0.447115 0.098558 0.096154
+4 0.824519 0.524038 0.086538 0.062500
+4 0.522837 0.368990 0.093750 0.026442
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_043.txt b/ai_train/retrain/data/stain/val/labels/kimchi_043.txt
new file mode 100644
index 000000000..072f1d327
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_043.txt
@@ -0,0 +1 @@
+4 0.530048 0.426683 0.069712 0.064904
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_050.txt b/ai_train/retrain/data/stain/val/labels/kimchi_050.txt
new file mode 100644
index 000000000..125d9aa33
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_050.txt
@@ -0,0 +1,3 @@
+4 0.555288 0.510817 0.225962 0.213942
+4 0.537260 0.784856 0.026442 0.026442
+4 0.555288 0.510817 0.307692 0.382212
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_057.txt b/ai_train/retrain/data/stain/val/labels/kimchi_057.txt
new file mode 100644
index 000000000..e469339ad
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_057.txt
@@ -0,0 +1,3 @@
+4 0.584135 0.453125 0.197115 0.189904
+4 0.475962 0.578125 0.057692 0.093750
+4 0.472356 0.450721 0.055288 0.093750
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_058.txt b/ai_train/retrain/data/stain/val/labels/kimchi_058.txt
new file mode 100644
index 000000000..807e2b265
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_058.txt
@@ -0,0 +1,3 @@
+4 0.230769 0.532452 0.048077 0.165865
+4 0.346154 0.614183 0.129808 0.156250
+4 0.725962 0.707933 0.091346 0.199519
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_062.txt b/ai_train/retrain/data/stain/val/labels/kimchi_062.txt
new file mode 100644
index 000000000..ac6a70468
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_062.txt
@@ -0,0 +1,8 @@
+4 0.545673 0.276442 0.052885 0.052885
+4 0.644231 0.324519 0.033654 0.057692
+4 0.581731 0.408654 0.100962 0.100962
+4 0.498798 0.360577 0.060096 0.062500
+4 0.481971 0.490385 0.036058 0.028846
+4 0.572115 0.492788 0.038462 0.048077
+4 0.593750 0.544471 0.038462 0.026442
+4 0.576923 0.354567 0.091346 0.045673
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_066.txt b/ai_train/retrain/data/stain/val/labels/kimchi_066.txt
new file mode 100644
index 000000000..cad6c4ac2
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_066.txt
@@ -0,0 +1,7 @@
+4 0.483173 0.320913 0.052885 0.060096
+4 0.554087 0.514423 0.151442 0.105769
+4 0.455529 0.647837 0.069712 0.108173
+4 0.355769 0.665865 0.091346 0.072115
+4 0.266827 0.872596 0.129808 0.076923
+4 0.163462 0.795673 0.038462 0.033654
+4 0.320913 0.753606 0.026442 0.016827
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_072.txt b/ai_train/retrain/data/stain/val/labels/kimchi_072.txt
new file mode 100644
index 000000000..5a3af7497
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_072.txt
@@ -0,0 +1,3 @@
+4 0.457933 0.596154 0.088942 0.110577
+4 0.533654 0.521635 0.019231 0.048077
+4 0.540865 0.468750 0.019231 0.019231
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_083.txt b/ai_train/retrain/data/stain/val/labels/kimchi_083.txt
new file mode 100644
index 000000000..c3a1995d3
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_083.txt
@@ -0,0 +1,3 @@
+4 0.361779 0.258413 0.045673 0.074519
+4 0.432692 0.358173 0.158654 0.177885
+4 0.429087 0.362981 0.108173 0.057692
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_089.txt b/ai_train/retrain/data/stain/val/labels/kimchi_089.txt
new file mode 100644
index 000000000..f1cd69be2
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_089.txt
@@ -0,0 +1,2 @@
+4 0.324519 0.591346 0.163462 0.086538
+4 0.353365 0.527644 0.052885 0.060096
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_090.txt b/ai_train/retrain/data/stain/val/labels/kimchi_090.txt
new file mode 100644
index 000000000..7c14cfce2
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_090.txt
@@ -0,0 +1,4 @@
+4 0.544471 0.472356 0.084135 0.069712
+4 0.697115 0.462740 0.028846 0.026442
+4 0.265625 0.457933 0.036058 0.026442
+4 0.080529 0.468750 0.060096 0.043269
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_095.txt b/ai_train/retrain/data/stain/val/labels/kimchi_095.txt
new file mode 100644
index 000000000..4e7ce22c1
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_095.txt
@@ -0,0 +1,6 @@
+4 0.147837 0.010817 0.036058 0.016827
+4 0.331731 0.271635 0.052885 0.072115
+4 0.033654 0.176683 0.019231 0.016827
+4 0.485577 0.531250 0.062500 0.091346
+4 0.479567 0.704327 0.031250 0.033654
+4 0.337740 0.826923 0.026442 0.024038
diff --git a/ai_train/retrain/data/stain/val/labels/kimchi_096.txt b/ai_train/retrain/data/stain/val/labels/kimchi_096.txt
new file mode 100644
index 000000000..e5db54238
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/kimchi_096.txt
@@ -0,0 +1,9 @@
+4 0.167067 0.275240 0.093750 0.088942
+4 0.155048 0.406250 0.117788 0.125000
+4 0.137019 0.212740 0.019231 0.016827
+4 0.185096 0.217548 0.024038 0.016827
+4 0.828125 0.323317 0.146635 0.141827
+4 0.754808 0.406250 0.028846 0.033654
+4 0.721154 0.441106 0.033654 0.040865
+4 0.052885 0.330529 0.033654 0.036058
+4 0.096154 0.313702 0.019231 0.031250
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_10.txt b/ai_train/retrain/data/stain/val/labels/lipstick_10.txt
new file mode 100644
index 000000000..6962099a0
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_10.txt
@@ -0,0 +1 @@
+5 0.640000 0.530055 0.109091 0.207650
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_16.txt b/ai_train/retrain/data/stain/val/labels/lipstick_16.txt
new file mode 100644
index 000000000..aee6775b1
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_16.txt
@@ -0,0 +1,3 @@
+5 0.449091 0.521858 0.185455 0.191257
+5 0.543636 0.428962 0.040000 0.060109
+5 0.643636 0.527322 0.065455 0.103825
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_18.txt b/ai_train/retrain/data/stain/val/labels/lipstick_18.txt
new file mode 100644
index 000000000..4a81a8c94
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_18.txt
@@ -0,0 +1 @@
+5 0.589091 0.669399 0.080000 0.060109
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_22.txt b/ai_train/retrain/data/stain/val/labels/lipstick_22.txt
new file mode 100644
index 000000000..174f6d8e8
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_22.txt
@@ -0,0 +1 @@
+5 0.708333 0.464883 0.071429 0.026756
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_31.txt b/ai_train/retrain/data/stain/val/labels/lipstick_31.txt
new file mode 100644
index 000000000..6d3aa9480
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_31.txt
@@ -0,0 +1 @@
+5 0.755952 0.576923 0.154762 0.103679
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_4.txt b/ai_train/retrain/data/stain/val/labels/lipstick_4.txt
new file mode 100644
index 000000000..5729d7851
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_4.txt
@@ -0,0 +1 @@
+5 0.437778 0.717778 0.448889 0.200000
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_40.txt b/ai_train/retrain/data/stain/val/labels/lipstick_40.txt
new file mode 100644
index 000000000..337c97d37
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_40.txt
@@ -0,0 +1 @@
+5 0.405462 0.554245 0.298319 0.221698
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_47.txt b/ai_train/retrain/data/stain/val/labels/lipstick_47.txt
new file mode 100644
index 000000000..fe76bc097
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_47.txt
@@ -0,0 +1,3 @@
+5 0.341085 0.520513 0.317829 0.282051
+5 0.288760 0.323077 0.127907 0.153846
+5 0.428295 0.402564 0.151163 0.138462
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_50.txt b/ai_train/retrain/data/stain/val/labels/lipstick_50.txt
new file mode 100644
index 000000000..c8a23530f
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_50.txt
@@ -0,0 +1,2 @@
+5 0.238235 0.469595 0.088235 0.222973
+5 0.316176 0.760135 0.197059 0.195946
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_51.txt b/ai_train/retrain/data/stain/val/labels/lipstick_51.txt
new file mode 100644
index 000000000..aad322427
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_51.txt
@@ -0,0 +1,2 @@
+5 0.315217 0.478142 0.094203 0.081967
+5 0.324275 0.442623 0.054348 0.054645
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_52.txt b/ai_train/retrain/data/stain/val/labels/lipstick_52.txt
new file mode 100644
index 000000000..ed3e7c5e3
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_52.txt
@@ -0,0 +1,3 @@
+5 0.356982 0.340708 0.069820 0.238938
+5 0.476351 0.128319 0.033784 0.115044
+5 0.581081 0.336283 0.166667 0.460177
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_54.txt b/ai_train/retrain/data/stain/val/labels/lipstick_54.txt
new file mode 100644
index 000000000..9e1f1ab85
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_54.txt
@@ -0,0 +1,7 @@
+5 0.587121 0.321990 0.166667 0.225131
+5 0.700758 0.565445 0.143939 0.104712
+5 0.717803 0.672775 0.178030 0.120419
+5 0.634470 0.764398 0.132576 0.104712
+5 0.609848 0.866492 0.196970 0.151832
+5 0.371212 0.717277 0.181818 0.104712
+5 0.371212 0.801047 0.151515 0.136126
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_59.txt b/ai_train/retrain/data/stain/val/labels/lipstick_59.txt
new file mode 100644
index 000000000..0566723a7
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_59.txt
@@ -0,0 +1 @@
+5 0.445000 0.508929 0.243333 0.279762
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_60.txt b/ai_train/retrain/data/stain/val/labels/lipstick_60.txt
new file mode 100644
index 000000000..2fe9fe9a5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_60.txt
@@ -0,0 +1,2 @@
+5 0.500000 0.590164 0.294545 0.349727
+5 0.552727 0.382514 0.101818 0.142077
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_66.txt b/ai_train/retrain/data/stain/val/labels/lipstick_66.txt
new file mode 100644
index 000000000..35f9887ff
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_66.txt
@@ -0,0 +1,4 @@
+5 0.715847 0.485455 0.185792 0.098182
+5 0.109290 0.474545 0.065574 0.025455
+5 0.284153 0.820000 0.065574 0.061818
+5 0.836066 0.825455 0.032787 0.072727
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_71.txt b/ai_train/retrain/data/stain/val/labels/lipstick_71.txt
new file mode 100644
index 000000000..b5067bc9c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_71.txt
@@ -0,0 +1 @@
+5 0.484496 0.366667 0.093023 0.117949
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_8.txt b/ai_train/retrain/data/stain/val/labels/lipstick_8.txt
new file mode 100644
index 000000000..a2b43398b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_8.txt
@@ -0,0 +1,3 @@
+5 0.494932 0.494118 0.077703 0.141176
+5 0.547297 0.635294 0.060811 0.105882
+5 0.596284 0.955882 0.050676 0.088235
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_85.txt b/ai_train/retrain/data/stain/val/labels/lipstick_85.txt
new file mode 100644
index 000000000..c85872e73
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_85.txt
@@ -0,0 +1 @@
+5 0.530909 0.450820 0.101818 0.136612
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_89.txt b/ai_train/retrain/data/stain/val/labels/lipstick_89.txt
new file mode 100644
index 000000000..bdf91befa
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_89.txt
@@ -0,0 +1 @@
+5 0.344262 0.263636 0.338798 0.207273
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_90.txt b/ai_train/retrain/data/stain/val/labels/lipstick_90.txt
new file mode 100644
index 000000000..1e12af151
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_90.txt
@@ -0,0 +1 @@
+5 0.380000 0.508889 0.244444 0.120000
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_91.txt b/ai_train/retrain/data/stain/val/labels/lipstick_91.txt
new file mode 100644
index 000000000..4bda92c98
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_91.txt
@@ -0,0 +1 @@
+5 0.583333 0.593333 0.357143 0.193333
diff --git a/ai_train/retrain/data/stain/val/labels/lipstick_93.txt b/ai_train/retrain/data/stain/val/labels/lipstick_93.txt
new file mode 100644
index 000000000..2ab767b39
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/lipstick_93.txt
@@ -0,0 +1,12 @@
+5 0.433333 0.571111 0.164444 0.146667
+5 0.562222 0.675556 0.164444 0.142222
+5 0.377778 0.806667 0.097778 0.102222
+5 0.571111 0.902222 0.111111 0.097778
+5 0.742222 0.366667 0.133333 0.111111
+5 0.960000 0.462222 0.071111 0.115556
+5 0.953333 0.717778 0.075556 0.137778
+5 0.755556 0.691111 0.062222 0.093333
+5 0.048889 0.802222 0.071111 0.093333
+5 0.051111 0.473333 0.057778 0.057778
+5 0.766667 0.228889 0.084444 0.057778
+5 0.713333 0.315556 0.128889 0.080000
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_003.txt b/ai_train/retrain/data/stain/val/labels/mustard_003.txt
new file mode 100644
index 000000000..d5ccb9ed5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_003.txt
@@ -0,0 +1,9 @@
+6 0.587329 0.248555 0.058219 0.046243
+6 0.642123 0.745665 0.133562 0.358382
+6 0.496575 0.907514 0.068493 0.127168
+4 0.270548 0.196532 0.041096 0.080925
+4 0.311644 0.306358 0.034247 0.080925
+4 0.304795 0.433526 0.041096 0.057803
+4 0.239726 0.560694 0.054795 0.104046
+4 0.210616 0.696532 0.044521 0.179191
+6 0.753425 0.696532 0.054795 0.121387
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_018.txt b/ai_train/retrain/data/stain/val/labels/mustard_018.txt
new file mode 100644
index 000000000..fb5a114b9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_018.txt
@@ -0,0 +1 @@
+6 0.485119 0.503333 0.244048 0.266667
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_024.txt b/ai_train/retrain/data/stain/val/labels/mustard_024.txt
new file mode 100644
index 000000000..9d94be63c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_024.txt
@@ -0,0 +1,6 @@
+6 0.289091 0.390710 0.076364 0.213115
+6 0.209091 0.568306 0.032727 0.054645
+6 0.312727 0.540984 0.036364 0.054645
+6 0.341818 0.633880 0.050909 0.109290
+6 0.363636 0.795082 0.036364 0.038251
+6 0.380000 0.860656 0.025455 0.038251
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_027.txt b/ai_train/retrain/data/stain/val/labels/mustard_027.txt
new file mode 100644
index 000000000..709d3e604
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_027.txt
@@ -0,0 +1,2 @@
+6 0.511905 0.353333 0.130952 0.060000
+6 0.452381 0.786667 0.047619 0.033333
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_045.txt b/ai_train/retrain/data/stain/val/labels/mustard_045.txt
new file mode 100644
index 000000000..9939d1c9e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_045.txt
@@ -0,0 +1 @@
+6 0.402222 0.893333 0.146667 0.186667
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_050.txt b/ai_train/retrain/data/stain/val/labels/mustard_050.txt
new file mode 100644
index 000000000..b8bcda5a2
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_050.txt
@@ -0,0 +1,4 @@
+6 0.401818 0.513661 0.323636 0.207650
+6 0.730909 0.450820 0.174545 0.234973
+6 0.641818 0.713115 0.149091 0.224044
+6 0.349091 0.606557 0.065455 0.131148
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_051.txt b/ai_train/retrain/data/stain/val/labels/mustard_051.txt
new file mode 100644
index 000000000..8fe603c13
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_051.txt
@@ -0,0 +1 @@
+6 0.672131 0.496364 0.065574 0.069091
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_053.txt b/ai_train/retrain/data/stain/val/labels/mustard_053.txt
new file mode 100644
index 000000000..de23ed534
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_053.txt
@@ -0,0 +1,2 @@
+6 0.637066 0.399485 0.115830 0.128866
+6 0.704633 0.768041 0.057915 0.103093
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_054.txt b/ai_train/retrain/data/stain/val/labels/mustard_054.txt
new file mode 100644
index 000000000..b84dcdb14
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_054.txt
@@ -0,0 +1,6 @@
+6 0.717778 0.271111 0.253333 0.204444
+6 0.495556 0.586667 0.084444 0.533333
+6 0.862222 0.813333 0.151111 0.364444
+6 0.608889 0.542222 0.782222 0.906667
+6 0.673333 0.557778 0.120000 0.066667
+6 0.835556 0.522222 0.106667 0.200000
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_065.txt b/ai_train/retrain/data/stain/val/labels/mustard_065.txt
new file mode 100644
index 000000000..cec5754d9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_065.txt
@@ -0,0 +1,4 @@
+6 0.412568 0.641818 0.081967 0.054545
+6 0.344262 0.776364 0.153005 0.178182
+6 0.486339 0.769091 0.054645 0.047273
+6 0.510929 0.670909 0.038251 0.047273
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_066.txt b/ai_train/retrain/data/stain/val/labels/mustard_066.txt
new file mode 100644
index 000000000..ce31e123a
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_066.txt
@@ -0,0 +1 @@
+6 0.511194 0.454787 0.067164 0.101064
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_074.txt b/ai_train/retrain/data/stain/val/labels/mustard_074.txt
new file mode 100644
index 000000000..59434df9f
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_074.txt
@@ -0,0 +1,6 @@
+6 0.218182 0.415301 0.225455 0.426230
+6 0.327273 0.603825 0.269091 0.355191
+6 0.541818 0.721311 0.298182 0.284153
+6 0.761818 0.650273 0.229091 0.207650
+6 0.240000 0.163934 0.123636 0.142077
+6 0.485455 0.513661 0.767273 0.972678
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_083.txt b/ai_train/retrain/data/stain/val/labels/mustard_083.txt
new file mode 100644
index 000000000..9ceace350
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_083.txt
@@ -0,0 +1,9 @@
+6 0.262548 0.172680 0.092664 0.128866
+6 0.370656 0.265464 0.115830 0.149485
+6 0.428571 0.376289 0.092664 0.103093
+6 0.484556 0.471649 0.042471 0.046392
+6 0.743243 0.579897 0.050193 0.087629
+6 0.656371 0.659794 0.108108 0.144330
+6 0.727799 0.690722 0.027027 0.041237
+6 0.799228 0.796392 0.069498 0.128866
+6 0.727799 0.804124 0.104247 0.144330
diff --git a/ai_train/retrain/data/stain/val/labels/mustard_084.txt b/ai_train/retrain/data/stain/val/labels/mustard_084.txt
new file mode 100644
index 000000000..a0766336c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/mustard_084.txt
@@ -0,0 +1,4 @@
+6 0.196429 0.590000 0.095238 0.060000
+6 0.455357 0.643333 0.089286 0.046667
+6 0.735119 0.721667 0.065476 0.063333
+6 0.666667 0.255000 0.071429 0.050000
diff --git a/ai_train/retrain/data/stain/val/labels/oil_001.txt b/ai_train/retrain/data/stain/val/labels/oil_001.txt
new file mode 100644
index 000000000..885916e72
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_001.txt
@@ -0,0 +1 @@
+7 0.750000 0.729167 0.393333 0.529762
diff --git a/ai_train/retrain/data/stain/val/labels/oil_003.txt b/ai_train/retrain/data/stain/val/labels/oil_003.txt
new file mode 100644
index 000000000..bccc0133b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_003.txt
@@ -0,0 +1 @@
+7 0.484556 0.440722 0.552124 0.314433
diff --git a/ai_train/retrain/data/stain/val/labels/oil_013.txt b/ai_train/retrain/data/stain/val/labels/oil_013.txt
new file mode 100644
index 000000000..446d33e86
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_013.txt
@@ -0,0 +1 @@
+7 0.590909 0.560109 0.156364 0.191257
diff --git a/ai_train/retrain/data/stain/val/labels/oil_015.txt b/ai_train/retrain/data/stain/val/labels/oil_015.txt
new file mode 100644
index 000000000..b7e4e2201
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_015.txt
@@ -0,0 +1,3 @@
+7 0.251366 0.738182 0.262295 0.240000
+7 0.584699 0.734545 0.371585 0.472727
+7 0.311475 0.436364 0.491803 0.174545
diff --git a/ai_train/retrain/data/stain/val/labels/oil_018.txt b/ai_train/retrain/data/stain/val/labels/oil_018.txt
new file mode 100644
index 000000000..b527c79c0
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_018.txt
@@ -0,0 +1,2 @@
+7 0.486622 0.866071 0.137124 0.220238
+7 0.687291 0.773810 0.090301 0.142857
diff --git a/ai_train/retrain/data/stain/val/labels/oil_022.txt b/ai_train/retrain/data/stain/val/labels/oil_022.txt
new file mode 100644
index 000000000..ec59efeae
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_022.txt
@@ -0,0 +1,2 @@
+7 0.557778 0.395556 0.164444 0.088889
+7 0.500000 0.753333 0.288889 0.315556
diff --git a/ai_train/retrain/data/stain/val/labels/oil_028.txt b/ai_train/retrain/data/stain/val/labels/oil_028.txt
new file mode 100644
index 000000000..adffd85ea
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_028.txt
@@ -0,0 +1,4 @@
+7 0.443452 0.528333 0.125000 0.096667
+7 0.392857 0.638333 0.130952 0.076667
+7 0.747024 0.306667 0.089286 0.073333
+7 0.729167 0.383333 0.113095 0.073333
diff --git a/ai_train/retrain/data/stain/val/labels/oil_034.txt b/ai_train/retrain/data/stain/val/labels/oil_034.txt
new file mode 100644
index 000000000..5f762d333
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_034.txt
@@ -0,0 +1 @@
+7 0.551237 0.199438 0.176678 0.252809
diff --git a/ai_train/retrain/data/stain/val/labels/oil_037.txt b/ai_train/retrain/data/stain/val/labels/oil_037.txt
new file mode 100644
index 000000000..4234ea66e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_037.txt
@@ -0,0 +1,3 @@
+7 0.455598 0.358247 0.123552 0.118557
+7 0.364865 0.582474 0.073359 0.092784
+7 0.563707 0.636598 0.054054 0.067010
diff --git a/ai_train/retrain/data/stain/val/labels/oil_077.txt b/ai_train/retrain/data/stain/val/labels/oil_077.txt
new file mode 100644
index 000000000..59d063736
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_077.txt
@@ -0,0 +1 @@
+7 0.198454 0.845560 0.345361 0.247104
diff --git a/ai_train/retrain/data/stain/val/labels/oil_078.txt b/ai_train/retrain/data/stain/val/labels/oil_078.txt
new file mode 100644
index 000000000..a2eaba60e
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_078.txt
@@ -0,0 +1,3 @@
+7 0.396907 0.416988 0.226804 0.200772
+7 0.533505 0.297297 0.190722 0.162162
+7 0.515464 0.637066 0.103093 0.123552
diff --git a/ai_train/retrain/data/stain/val/labels/oil_079.txt b/ai_train/retrain/data/stain/val/labels/oil_079.txt
new file mode 100644
index 000000000..49f583af4
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_079.txt
@@ -0,0 +1 @@
+7 0.438144 0.449807 0.185567 0.150579
diff --git a/ai_train/retrain/data/stain/val/labels/oil_081.txt b/ai_train/retrain/data/stain/val/labels/oil_081.txt
new file mode 100644
index 000000000..50e5677c0
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_081.txt
@@ -0,0 +1 @@
+7 0.492268 0.583012 0.190722 0.247104
diff --git a/ai_train/retrain/data/stain/val/labels/oil_096.txt b/ai_train/retrain/data/stain/val/labels/oil_096.txt
new file mode 100644
index 000000000..b50e47224
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/oil_096.txt
@@ -0,0 +1,4 @@
+7 0.800000 0.342014 0.171429 0.100694
+7 0.711429 0.612847 0.337143 0.184028
+7 0.422857 0.590278 0.148571 0.090278
+7 0.705714 0.835069 0.108571 0.079861
diff --git a/ai_train/retrain/data/stain/val/labels/wine_006.txt b/ai_train/retrain/data/stain/val/labels/wine_006.txt
new file mode 100644
index 000000000..76d992d8b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_006.txt
@@ -0,0 +1 @@
+8 0.574545 0.295082 0.312727 0.229508
diff --git a/ai_train/retrain/data/stain/val/labels/wine_007.txt b/ai_train/retrain/data/stain/val/labels/wine_007.txt
new file mode 100644
index 000000000..8a1a839a9
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_007.txt
@@ -0,0 +1,2 @@
+8 0.254545 0.472678 0.145455 0.202186
+8 0.321818 0.849727 0.200000 0.300546
diff --git a/ai_train/retrain/data/stain/val/labels/wine_023.txt b/ai_train/retrain/data/stain/val/labels/wine_023.txt
new file mode 100644
index 000000000..2bc6918e3
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_023.txt
@@ -0,0 +1 @@
+8 0.403010 0.723214 0.143813 0.136905
diff --git a/ai_train/retrain/data/stain/val/labels/wine_025.txt b/ai_train/retrain/data/stain/val/labels/wine_025.txt
new file mode 100644
index 000000000..f684bc140
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_025.txt
@@ -0,0 +1,3 @@
+8 0.675000 0.571429 0.043333 0.107143
+8 0.655000 0.699405 0.043333 0.089286
+8 0.646667 0.806548 0.053333 0.077381
diff --git a/ai_train/retrain/data/stain/val/labels/wine_026.txt b/ai_train/retrain/data/stain/val/labels/wine_026.txt
new file mode 100644
index 000000000..eae92870c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_026.txt
@@ -0,0 +1,4 @@
+8 0.609434 0.418421 0.215094 0.363158
+8 0.554717 0.678947 0.233962 0.242105
+8 0.722642 0.563158 0.079245 0.442105
+8 0.866038 0.423684 0.109434 0.152632
diff --git a/ai_train/retrain/data/stain/val/labels/wine_027.txt b/ai_train/retrain/data/stain/val/labels/wine_027.txt
new file mode 100644
index 000000000..dc9e7ace0
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_027.txt
@@ -0,0 +1,3 @@
+8 0.441818 0.480874 0.265455 0.404372
+8 0.545455 0.448087 0.036364 0.087432
+8 0.652727 0.532787 0.149091 0.114754
diff --git a/ai_train/retrain/data/stain/val/labels/wine_032.txt b/ai_train/retrain/data/stain/val/labels/wine_032.txt
new file mode 100644
index 000000000..154025b81
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_032.txt
@@ -0,0 +1 @@
+8 0.520000 0.644809 0.290909 0.163934
diff --git a/ai_train/retrain/data/stain/val/labels/wine_044.txt b/ai_train/retrain/data/stain/val/labels/wine_044.txt
new file mode 100644
index 000000000..a73db417b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_044.txt
@@ -0,0 +1,2 @@
+8 0.451737 0.221649 0.084942 0.123711
+8 0.774131 0.293814 0.081081 0.103093
diff --git a/ai_train/retrain/data/stain/val/labels/wine_049.txt b/ai_train/retrain/data/stain/val/labels/wine_049.txt
new file mode 100644
index 000000000..d2c64ac18
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_049.txt
@@ -0,0 +1 @@
+8 0.345455 0.756831 0.276364 0.355191
diff --git a/ai_train/retrain/data/stain/val/labels/wine_051.txt b/ai_train/retrain/data/stain/val/labels/wine_051.txt
new file mode 100644
index 000000000..1fd776a0d
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_051.txt
@@ -0,0 +1 @@
+8 0.600583 0.302721 0.064140 0.346939
diff --git a/ai_train/retrain/data/stain/val/labels/wine_052.txt b/ai_train/retrain/data/stain/val/labels/wine_052.txt
new file mode 100644
index 000000000..4ea4e23e4
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_052.txt
@@ -0,0 +1,2 @@
+8 0.363806 0.321809 0.108209 0.196809
+8 0.363806 0.585106 0.070896 0.095745
diff --git a/ai_train/retrain/data/stain/val/labels/wine_053.txt b/ai_train/retrain/data/stain/val/labels/wine_053.txt
new file mode 100644
index 000000000..86eae1c7b
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_053.txt
@@ -0,0 +1,6 @@
+8 0.218750 0.394578 0.101974 0.162651
+8 0.273026 0.563253 0.092105 0.271084
+8 0.304276 0.756024 0.108553 0.150602
+8 0.536184 0.864458 0.256579 0.090361
+8 0.720395 0.650602 0.065789 0.349398
+8 0.366776 0.301205 0.121711 0.084337
diff --git a/ai_train/retrain/data/stain/val/labels/wine_055.txt b/ai_train/retrain/data/stain/val/labels/wine_055.txt
new file mode 100644
index 000000000..fb96b8b94
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_055.txt
@@ -0,0 +1 @@
+8 0.312709 0.291667 0.056856 0.095238
diff --git a/ai_train/retrain/data/stain/val/labels/wine_057.txt b/ai_train/retrain/data/stain/val/labels/wine_057.txt
new file mode 100644
index 000000000..1a0ed1e41
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_057.txt
@@ -0,0 +1,3 @@
+8 0.676364 0.366120 0.065455 0.142077
+8 0.605455 0.606557 0.178182 0.240437
+8 0.672727 0.775956 0.065455 0.109290
diff --git a/ai_train/retrain/data/stain/val/labels/wine_062.txt b/ai_train/retrain/data/stain/val/labels/wine_062.txt
new file mode 100644
index 000000000..37055c32a
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_062.txt
@@ -0,0 +1,5 @@
+8 0.696335 0.676136 0.188482 0.094697
+8 0.481675 0.821970 0.115183 0.219697
+8 0.549738 0.818182 0.115183 0.181818
+8 0.722513 0.840909 0.157068 0.196970
+8 0.387435 0.566288 0.052356 0.026515
diff --git a/ai_train/retrain/data/stain/val/labels/wine_068.txt b/ai_train/retrain/data/stain/val/labels/wine_068.txt
new file mode 100644
index 000000000..0dd3df904
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_068.txt
@@ -0,0 +1,2 @@
+8 0.592727 0.644809 0.196364 0.557377
+8 0.541818 0.775956 0.254545 0.295082
diff --git a/ai_train/retrain/data/stain/val/labels/wine_072.txt b/ai_train/retrain/data/stain/val/labels/wine_072.txt
new file mode 100644
index 000000000..02d859eb2
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_072.txt
@@ -0,0 +1 @@
+8 0.280822 0.427746 0.171233 0.751445
diff --git a/ai_train/retrain/data/stain/val/labels/wine_074.txt b/ai_train/retrain/data/stain/val/labels/wine_074.txt
new file mode 100644
index 000000000..e2f88fdff
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_074.txt
@@ -0,0 +1,5 @@
+8 0.488462 0.074742 0.046154 0.056701
+8 0.628846 0.188144 0.280769 0.077320
+8 0.409615 0.577320 0.119231 0.154639
+8 0.776923 0.505155 0.215385 0.134021
+8 0.786538 0.706186 0.165385 0.226804
diff --git a/ai_train/retrain/data/stain/val/labels/wine_077.txt b/ai_train/retrain/data/stain/val/labels/wine_077.txt
new file mode 100644
index 000000000..970d01962
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_077.txt
@@ -0,0 +1,3 @@
+8 0.603636 0.483607 0.414545 0.256831
+8 0.267273 0.639344 0.083636 0.306011
+8 0.523636 0.625683 0.116364 0.169399
diff --git a/ai_train/retrain/data/stain/val/labels/wine_082.txt b/ai_train/retrain/data/stain/val/labels/wine_082.txt
new file mode 100644
index 000000000..f310ae200
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_082.txt
@@ -0,0 +1,4 @@
+8 0.605000 0.422619 0.490000 0.452381
+8 0.573333 0.729167 0.320000 0.541667
+8 0.835000 0.610119 0.330000 0.446429
+8 0.693333 0.151786 0.286667 0.184524
diff --git a/ai_train/retrain/data/stain/val/labels/wine_083.txt b/ai_train/retrain/data/stain/val/labels/wine_083.txt
new file mode 100644
index 000000000..b722c4715
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_083.txt
@@ -0,0 +1,7 @@
+8 0.685567 0.463320 0.154639 0.115830
+8 0.481959 0.482625 0.108247 0.084942
+8 0.206186 0.671815 0.103093 0.061776
+8 0.608247 0.146718 0.082474 0.061776
+8 0.551546 0.723938 0.185567 0.081081
+8 0.597938 0.650579 0.041237 0.281853
+8 0.548969 0.847490 0.067010 0.042471
diff --git a/ai_train/retrain/data/stain/val/labels/wine_086.txt b/ai_train/retrain/data/stain/val/labels/wine_086.txt
new file mode 100644
index 000000000..ccfe4bbe5
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_086.txt
@@ -0,0 +1 @@
+8 0.472973 0.427835 0.104247 0.082474
diff --git a/ai_train/retrain/data/stain/val/labels/wine_091.txt b/ai_train/retrain/data/stain/val/labels/wine_091.txt
new file mode 100644
index 000000000..299d30797
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_091.txt
@@ -0,0 +1,5 @@
+8 0.251667 0.407738 0.256667 0.327381
+8 0.725000 0.330357 0.543333 0.184524
+8 0.588333 0.580357 0.223333 0.482143
+8 0.241667 0.735119 0.410000 0.529762
+8 0.740000 0.758929 0.306667 0.482143
diff --git a/ai_train/retrain/data/stain/val/labels/wine_093.txt b/ai_train/retrain/data/stain/val/labels/wine_093.txt
new file mode 100644
index 000000000..fdc084f97
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_093.txt
@@ -0,0 +1,4 @@
+8 0.293814 0.279923 0.195876 0.119691
+8 0.451031 0.386100 0.242268 0.138996
+8 0.659794 0.494208 0.257732 0.123552
+8 0.824742 0.571429 0.226804 0.131274
diff --git a/ai_train/retrain/data/stain/val/labels/wine_095.txt b/ai_train/retrain/data/stain/val/labels/wine_095.txt
new file mode 100644
index 000000000..7b9a6330c
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_095.txt
@@ -0,0 +1 @@
+8 0.536364 0.543716 0.214545 0.311475
diff --git a/ai_train/retrain/data/stain/val/labels/wine_099.txt b/ai_train/retrain/data/stain/val/labels/wine_099.txt
new file mode 100644
index 000000000..54e94e302
--- /dev/null
+++ b/ai_train/retrain/data/stain/val/labels/wine_099.txt
@@ -0,0 +1 @@
+8 0.427336 0.540230 0.169550 0.436782
diff --git a/ai_train/retrain/data/symbol/data.yaml b/ai_train/retrain/data/symbol/data.yaml
new file mode 100644
index 000000000..05209aa52
--- /dev/null
+++ b/ai_train/retrain/data/symbol/data.yaml
@@ -0,0 +1,49 @@
+train:  /home/t25119/aiLab/test/rushWash/ai/data/symbol/train/images
+val:  /home/t25119/aiLab/test/rushWash/ai/data/symbol/val/images
+test:  /home/t25119/aiLab/test/rushWash/ai/data/symbol/test/images
+
+nc: 43
+names:
+  - 30C                         # 0
+  - 40C                         # 1
+  - 50C                         # 2
+  - 60C                         # 3
+  - 70C                         # 4
+  - 95C                         # 5
+  - DN_bleach                   # 6
+  - DN_dry                      # 7
+  - DN_dry_clean                # 8
+  - DN_iron                     # 9
+  - DN_steam                    # 10
+  - DN_tumble_dry               # 11
+  - DN_wash                     # 12
+  - DN_wet_clean                # 13
+  - DN_wring                    # 14
+  - bleach                      # 15
+  - chlorine_bleach             # 16
+  - non_chlorine_bleach         # 17
+  - drip_dry                    # 18
+  - drip_dry_in_shade           # 19
+  - dry_flat                    # 20
+  - dry_flat_in_shade           # 21
+  - line_dry                    # 22
+  - line_dry_in_shade           # 23
+  - natural_dry                 # 24
+  - shade_dry                   # 25
+  - dry_clean                   # 26   ← 통합됨 (원래 26+27)
+  - dry_clean_any_solvent_except_trichloroethylene   # 27
+  - dry_clean_petrol_only       # 28
+  - hand_wash                   # 29
+  - machine_wash                # 30
+  - iron                        # 31
+  - iron_high                   # 32
+  - iron_medium                 # 33
+  - iron_low                    # 34
+  - steam                       # 35
+  - tumble_dry_normal           # 36
+  - tumble_dry_low              # 37
+  - tumble_dry_medium           # 38
+  - tumble_dry_high             # 39
+  - tumble_dry_no_heat          # 40
+  - wet_clean                   # 41
+  - wring                       # 42
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_111821393_10.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_111821393_10.txt
new file mode 100644
index 000000000..97704f38b
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_111821393_10.txt
@@ -0,0 +1,5 @@
+29 0.392526 0.899926 0.076720 0.060764
+17 0.475198 0.902282 0.078042 0.063988
+33 0.560516 0.901910 0.080026 0.056796
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+23 0.713624 0.898810 0.083333 0.060020
diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372.txt
new file mode 100644
index 000000000..38ff1b5ce
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372.txt
@@ -0,0 +1,5 @@
+12 0.339352 0.523264 0.095370 0.075694
+6 0.439815 0.515972 0.088889 0.080556
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+23 0.742130 0.507986 0.095370 0.070139
diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_01.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_01.txt
new file mode 100644
index 000000000..2257710f8
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_01.txt
@@ -0,0 +1,6 @@
+29 0.172685 0.240278 0.117593 0.097222
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_02.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_02.txt
new file mode 100644
index 000000000..cd4e7544f
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_02.txt
@@ -0,0 +1,5 @@
+29 0.192130 0.250000 0.134259 0.088889
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_03.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_03.txt
new file mode 100644
index 000000000..ee1ad13a5
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_03.txt
@@ -0,0 +1,5 @@
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+6 0.330093 0.284375 0.123148 0.090972
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+23 0.666667 0.285764 0.111111 0.088194
diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_04.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_04.txt
new file mode 100644
index 000000000..aa8dc4f10
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_04.txt
@@ -0,0 +1,4 @@
+0 0.317177 0.586503 0.113946 0.093252
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+36 0.715136 0.585276 0.110544 0.078528
diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_05.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_05.txt
new file mode 100644
index 000000000..8df95b285
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_05.txt
@@ -0,0 +1,6 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_06.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_06.txt
new file mode 100644
index 000000000..ba8585882
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_06.txt
@@ -0,0 +1,7 @@
+1 0.129167 0.192361 0.117593 0.093056
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_07.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_07.txt
new file mode 100644
index 000000000..b9e983ddb
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_07.txt
@@ -0,0 +1,7 @@
+29 0.271759 0.351042 0.084259 0.064583
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_08.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_08.txt
new file mode 100644
index 000000000..a265206e9
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_08.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_09.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_09.txt
new file mode 100644
index 000000000..6f997ed49
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_09.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_10.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_10.txt
new file mode 100644
index 000000000..11326d76a
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_144454372_10.txt
@@ -0,0 +1,6 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765.txt
new file mode 100644
index 000000000..95d8f6149
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_01.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_01.txt
new file mode 100644
index 000000000..2406cfb4d
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_01.txt
@@ -0,0 +1,11 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_02.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_02.txt
new file mode 100644
index 000000000..97effdef4
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_02.txt
@@ -0,0 +1,4 @@
+30 0.362963 0.297569 0.096296 0.065972
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_03.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_03.txt
new file mode 100644
index 000000000..4aec88ab3
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_03.txt
@@ -0,0 +1,4 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_04.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_04.txt
new file mode 100644
index 000000000..71ef054af
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_04.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_05.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_05.txt
new file mode 100644
index 000000000..a3c9e7c4c
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_05.txt
@@ -0,0 +1,7 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_06.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_06.txt
new file mode 100644
index 000000000..d66e7cb40
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_06.txt
@@ -0,0 +1,4 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_07.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_07.txt
new file mode 100644
index 000000000..6f9259310
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_07.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_08.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_08.txt
new file mode 100644
index 000000000..3a92721e9
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_08.txt
@@ -0,0 +1,4 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_09.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_09.txt
new file mode 100644
index 000000000..e9c678590
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_09.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_10.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_10.txt
new file mode 100644
index 000000000..b76cf393a
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_221400765_10.txt
@@ -0,0 +1,4 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225.txt
new file mode 100644
index 000000000..a7e6b4a98
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225.txt
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_01.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_01.txt
new file mode 100644
index 000000000..f3239b437
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_01.txt
@@ -0,0 +1,6 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_02.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_02.txt
new file mode 100644
index 000000000..f2b1d1f12
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_02.txt
@@ -0,0 +1,5 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_03.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_03.txt
new file mode 100644
index 000000000..e051c5b2e
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_03.txt
@@ -0,0 +1,15 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_04.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_04.txt
new file mode 100644
index 000000000..e1fd45b49
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_04.txt
@@ -0,0 +1,6 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_05.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_05.txt
new file mode 100644
index 000000000..260f7c86c
--- /dev/null
+++ b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_05.txt
@@ -0,0 +1,6 @@
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_06.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_06.txt
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_07.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_07.txt
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_09.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_09.txt
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diff --git a/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_10.txt b/ai_train/retrain/data/symbol/test/labels/KakaoTalk_20250516_223034225_10.txt
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+11 0.548958 0.435417 0.047917 0.037500
+8 0.613542 0.409375 0.039583 0.039583
+9 0.671875 0.427083 0.068750 0.054167
+12 0.656250 0.477083 0.066667 0.054167
+6 0.598958 0.455208 0.052083 0.043750
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+1 0.646875 0.284375 0.097917 0.093750
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+9 0.530208 0.221875 0.097917 0.089583
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+11 0.439583 0.332292 0.091667 0.093750
+42 0.331250 0.348958 0.100000 0.072917
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+9 0.384375 0.314583 0.110417 0.100000
+17 0.451042 0.410417 0.072917 0.100000
+23 0.500000 0.496875 0.066667 0.093750
+9 0.563542 0.578125 0.089583 0.089583
+28 0.631250 0.669792 0.095833 0.093750
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+12 0.512500 0.310417 0.116667 0.100000
+6 0.591667 0.375000 0.095833 0.079167
+34 0.673958 0.438542 0.093750 0.064583
+28 0.751042 0.520833 0.114583 0.087500
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index 000000000..95e1b2866
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@@ -0,0 +1,15 @@
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+6 0.419792 0.416667 0.081250 0.058333
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+11 0.581250 0.423958 0.083333 0.056250
+21 0.670833 0.426042 0.087500 0.064583
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+6 0.416667 0.488542 0.079167 0.056250
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diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00030.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00030.txt
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index 000000000..48989ca8c
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+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00030.txt
@@ -0,0 +1,5 @@
+1 0.391667 0.662500 0.100000 0.087500
+17 0.418750 0.581250 0.083333 0.066667
+9 0.437500 0.504167 0.079167 0.091667
+8 0.460417 0.402083 0.095833 0.095833
+23 0.492708 0.304167 0.081250 0.079167
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00049.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00049.txt
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index 000000000..d9a83c5c7
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+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00049.txt
@@ -0,0 +1,4 @@
+30 0.170833 0.514583 0.129167 0.112500
+26 0.321875 0.465625 0.093750 0.143750
+21 0.478125 0.405208 0.131250 0.102083
+6 0.634375 0.354167 0.143750 0.100000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00052.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00052.txt
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index 000000000..600f8ca11
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+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00052.txt
@@ -0,0 +1,4 @@
+21 0.239583 0.221875 0.191667 0.114583
+14 0.457292 0.303125 0.210417 0.106250
+17 0.671875 0.393750 0.160417 0.141667
+29 0.884375 0.477083 0.189583 0.120833
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00057.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00057.txt
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index 000000000..47612a12a
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00057.txt
@@ -0,0 +1,2 @@
+6 0.190625 0.193750 0.177083 0.141667
+9 0.083333 0.368750 0.162500 0.191667
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00062.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00062.txt
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index 000000000..fc819282f
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00062.txt
@@ -0,0 +1,5 @@
+0 0.120833 0.769792 0.104167 0.114583
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+31 0.587500 0.771875 0.141667 0.097917
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diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00077.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00077.txt
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index 000000000..0b259ea4f
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00077.txt
@@ -0,0 +1,4 @@
+12 0.213542 0.784375 0.085417 0.089583
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diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00079.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00079.txt
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index 000000000..be5f28001
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00079.txt
@@ -0,0 +1,6 @@
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+11 0.439583 0.870833 0.075000 0.108333
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00097.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00097.txt
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index 000000000..a39e08afc
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00097.txt
@@ -0,0 +1,6 @@
+12 0.266667 0.478125 0.058333 0.081250
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+13 0.585417 0.700000 0.070833 0.075000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00110.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00110.txt
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index 000000000..a0aece579
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00110.txt
@@ -0,0 +1,5 @@
+0 0.623958 0.218750 0.052083 0.062500
+17 0.640625 0.276042 0.056250 0.047917
+37 0.657292 0.321875 0.047917 0.052083
+34 0.669792 0.372917 0.060417 0.062500
+8 0.695833 0.425000 0.050000 0.054167
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00112.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00112.txt
new file mode 100644
index 000000000..3ca10b8dc
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00112.txt
@@ -0,0 +1,5 @@
+30 0.388542 0.413542 0.056250 0.060417
+6 0.461458 0.414583 0.056250 0.054167
+9 0.537500 0.414583 0.062500 0.062500
+8 0.610417 0.420833 0.075000 0.062500
+11 0.685417 0.418750 0.066667 0.058333
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00116.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00116.txt
new file mode 100644
index 000000000..c6f9eb6d9
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00116.txt
@@ -0,0 +1,4 @@
+23 0.376042 0.475000 0.097917 0.095833
+33 0.387500 0.389583 0.075000 0.079167
+17 0.382292 0.310417 0.089583 0.070833
+29 0.400000 0.217708 0.083333 0.093750
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00123.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00123.txt
new file mode 100644
index 000000000..4689e4f1c
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00123.txt
@@ -0,0 +1,5 @@
+37 0.229167 0.657292 0.154167 0.152083
+21 0.380208 0.604167 0.131250 0.137500
+11 0.531250 0.541667 0.150000 0.154167
+6 0.681250 0.482292 0.125000 0.185417
+26 0.821875 0.410417 0.127083 0.154167
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00127.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00127.txt
new file mode 100644
index 000000000..dfe8604f4
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00127.txt
@@ -0,0 +1,6 @@
+12 0.231250 0.270833 0.170833 0.166667
+17 0.428125 0.345833 0.168750 0.129167
+23 0.320833 0.462500 0.204167 0.154167
+34 0.628125 0.443750 0.247917 0.145833
+11 0.655208 0.589583 0.197917 0.154167
+28 0.889583 0.518750 0.158333 0.187500
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00137.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00137.txt
new file mode 100644
index 000000000..2f933f1a6
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00137.txt
@@ -0,0 +1,6 @@
+29 0.137500 0.537500 0.145833 0.125000
+6 0.291667 0.533333 0.150000 0.129167
+33 0.435417 0.533333 0.129167 0.116667
+8 0.567708 0.520833 0.135417 0.133333
+42 0.701042 0.514583 0.131250 0.095833
+23 0.833333 0.505208 0.125000 0.114583
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00149.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00149.txt
new file mode 100644
index 000000000..cc908ff63
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00149.txt
@@ -0,0 +1,5 @@
+29 0.306250 0.219792 0.170833 0.168750
+6 0.425000 0.350000 0.175000 0.179167
+34 0.543750 0.483333 0.162500 0.154167
+42 0.665625 0.601042 0.168750 0.135417
+11 0.780208 0.735417 0.197917 0.183333
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00157.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00157.txt
new file mode 100644
index 000000000..a4bee317f
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00157.txt
@@ -0,0 +1,5 @@
+29 0.150000 0.620833 0.129167 0.137500
+17 0.301042 0.619792 0.131250 0.147917
+9 0.447917 0.633333 0.133333 0.133333
+42 0.607292 0.631250 0.135417 0.079167
+23 0.758333 0.635417 0.120833 0.125000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00160.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00160.txt
new file mode 100644
index 000000000..b3e89a3df
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00160.txt
@@ -0,0 +1,4 @@
+29 0.332292 0.239583 0.131250 0.137500
+17 0.347917 0.428125 0.125000 0.172917
+23 0.404167 0.592708 0.120833 0.114583
+14 0.456250 0.757292 0.112500 0.139583
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00175.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00175.txt
new file mode 100644
index 000000000..cf5e48fcd
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00175.txt
@@ -0,0 +1,5 @@
+29 0.455208 0.871875 0.181250 0.181250
+17 0.469792 0.684375 0.156250 0.168750
+34 0.468750 0.504167 0.145833 0.170833
+11 0.485417 0.329167 0.154167 0.141667
+8 0.490625 0.158333 0.160417 0.141667
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00178.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00178.txt
new file mode 100644
index 000000000..c7a08db77
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00178.txt
@@ -0,0 +1,4 @@
+29 0.633333 0.741667 0.095833 0.162500
+17 0.709375 0.806250 0.114583 0.158333
+23 0.841667 0.836458 0.116667 0.156250
+11 0.646875 0.948958 0.110417 0.102083
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00179.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00179.txt
new file mode 100644
index 000000000..dffcb389e
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00179.txt
@@ -0,0 +1,5 @@
+0 0.551042 0.559375 0.060417 0.052083
+9 0.602083 0.619792 0.075000 0.060417
+11 0.656250 0.675000 0.079167 0.062500
+6 0.705208 0.745833 0.068750 0.066667
+8 0.755208 0.805208 0.064583 0.056250
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00187.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00187.txt
new file mode 100644
index 000000000..8716d5730
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00187.txt
@@ -0,0 +1,15 @@
+29 0.332292 0.540625 0.064583 0.047917
+6 0.416667 0.536458 0.066667 0.047917
+33 0.488542 0.534375 0.064583 0.047917
+21 0.564583 0.533333 0.070833 0.054167
+11 0.641667 0.525000 0.070833 0.050000
+29 0.338542 0.616667 0.081250 0.058333
+29 0.351042 0.708333 0.072917 0.054167
+6 0.423958 0.612500 0.072917 0.050000
+6 0.431250 0.707292 0.062500 0.052083
+33 0.503125 0.605208 0.064583 0.052083
+33 0.589583 0.695833 0.075000 0.045833
+20 0.510417 0.698958 0.062500 0.052083
+20 0.575000 0.604167 0.066667 0.050000
+11 0.654167 0.600000 0.079167 0.054167
+8 0.672917 0.689583 0.075000 0.050000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00189.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00189.txt
new file mode 100644
index 000000000..0c4db1a4f
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00189.txt
@@ -0,0 +1,15 @@
+26 0.135417 0.789583 0.037500 0.045833
+11 0.175000 0.782292 0.041667 0.047917
+31 0.216667 0.778125 0.033333 0.035417
+6 0.251042 0.783333 0.031250 0.041667
+12 0.288542 0.782292 0.035417 0.039583
+12 0.584375 0.779167 0.039583 0.050000
+12 0.869792 0.772917 0.039583 0.041667
+6 0.547917 0.780208 0.037500 0.043750
+6 0.830208 0.772917 0.035417 0.045833
+31 0.511458 0.779167 0.031250 0.041667
+31 0.794792 0.769792 0.035417 0.043750
+11 0.471875 0.779167 0.043750 0.045833
+11 0.757292 0.770833 0.039583 0.045833
+26 0.434375 0.780208 0.039583 0.039583
+26 0.716667 0.772917 0.045833 0.045833
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00193.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00193.txt
new file mode 100644
index 000000000..281256f76
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00193.txt
@@ -0,0 +1,5 @@
+6 0.359375 0.416667 0.072917 0.070833
+34 0.428125 0.420833 0.064583 0.070833
+26 0.495833 0.427083 0.062500 0.075000
+14 0.568750 0.434375 0.075000 0.072917
+23 0.641667 0.443750 0.066667 0.066667
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00196.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00196.txt
new file mode 100644
index 000000000..8994701c5
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00196.txt
@@ -0,0 +1,5 @@
+8 0.356250 0.338542 0.100000 0.114583
+25 0.432292 0.390625 0.093750 0.093750
+6 0.583333 0.491667 0.087500 0.091667
+31 0.498958 0.441667 0.081250 0.095833
+0 0.651042 0.558333 0.077083 0.091667
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00199.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00199.txt
new file mode 100644
index 000000000..ba38715e5
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00199.txt
@@ -0,0 +1,12 @@
+12 0.472917 0.251042 0.037500 0.043750
+12 0.478125 0.435417 0.043750 0.045833
+12 0.468750 0.633333 0.041667 0.041667
+17 0.472917 0.284375 0.045833 0.035417
+17 0.476042 0.473958 0.039583 0.031250
+17 0.468750 0.668750 0.041667 0.033333
+34 0.473958 0.320833 0.035417 0.037500
+34 0.479167 0.506250 0.045833 0.033333
+34 0.470833 0.705208 0.041667 0.031250
+21 0.475000 0.355208 0.041667 0.031250
+21 0.481250 0.541667 0.037500 0.037500
+21 0.470833 0.741667 0.037500 0.033333
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00200.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00200.txt
new file mode 100644
index 000000000..42fab9126
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00200.txt
@@ -0,0 +1,5 @@
+1 0.542708 0.203125 0.077083 0.060417
+6 0.601042 0.258333 0.064583 0.070833
+11 0.666667 0.307292 0.070833 0.077083
+33 0.735417 0.359375 0.070833 0.068750
+8 0.798958 0.407292 0.068750 0.060417
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00206.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00206.txt
new file mode 100644
index 000000000..1873ceb7c
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00206.txt
@@ -0,0 +1,12 @@
+26 0.621875 0.037500 0.043750 0.041667
+6 0.677083 0.056250 0.045833 0.041667
+11 0.740625 0.072917 0.056250 0.045833
+9 0.800000 0.090625 0.050000 0.047917
+12 0.861458 0.109375 0.052083 0.039583
+12 0.452083 0.432292 0.045833 0.027083
+12 0.460417 0.406250 0.041667 0.029167
+27 0.407292 0.430208 0.039583 0.022917
+27 0.415625 0.403125 0.027083 0.027083
+6 0.502083 0.434375 0.045833 0.031250
+11 0.548958 0.434375 0.047917 0.031250
+9 0.601042 0.438542 0.043750 0.031250
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00207.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00207.txt
new file mode 100644
index 000000000..edba9f784
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00207.txt
@@ -0,0 +1,10 @@
+28 0.176042 0.541667 0.072917 0.075000
+6 0.222917 0.592708 0.070833 0.068750
+33 0.262500 0.636458 0.079167 0.072917
+21 0.296875 0.682292 0.072917 0.068750
+11 0.333333 0.730208 0.070833 0.081250
+11 0.239583 0.817708 0.079167 0.064583
+20 0.198958 0.770833 0.077083 0.070833
+33 0.155208 0.730208 0.068750 0.060417
+6 0.110417 0.687500 0.075000 0.066667
+28 0.071875 0.635417 0.072917 0.066667
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00211.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00211.txt
new file mode 100644
index 000000000..9dc62a20b
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00211.txt
@@ -0,0 +1,22 @@
+27 0.391667 0.379167 0.062500 0.066667
+27 0.470833 0.470833 0.066667 0.066667
+27 0.610417 0.662500 0.062500 0.075000
+34 0.436458 0.355208 0.052083 0.068750
+34 0.506250 0.444792 0.070833 0.068750
+34 0.658333 0.630208 0.062500 0.060417
+34 0.722917 0.727083 0.070833 0.079167
+34 0.642708 0.542708 0.068750 0.077083
+37 0.503125 0.344792 0.068750 0.089583
+37 0.567708 0.429167 0.085417 0.087500
+37 0.706250 0.610417 0.062500 0.066667
+37 0.773958 0.697917 0.060417 0.070833
+17 0.532292 0.298958 0.060417 0.064583
+17 0.600000 0.385417 0.058333 0.066667
+17 0.683333 0.492708 0.062500 0.064583
+17 0.742708 0.573958 0.060417 0.056250
+17 0.806250 0.656250 0.058333 0.062500
+1 0.589583 0.281250 0.066667 0.079167
+1 0.663542 0.368750 0.081250 0.083333
+1 0.735417 0.460417 0.062500 0.087500
+1 0.793750 0.537500 0.079167 0.087500
+1 0.868750 0.635417 0.087500 0.104167
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00213.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00213.txt
new file mode 100644
index 000000000..2bdb1294e
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00213.txt
@@ -0,0 +1,3 @@
+29 0.430208 0.403125 0.085417 0.102083
+17 0.528125 0.396875 0.093750 0.127083
+42 0.639583 0.398958 0.112500 0.089583
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00218.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00218.txt
new file mode 100644
index 000000000..176bbaa3d
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00218.txt
@@ -0,0 +1,5 @@
+1 0.141667 0.418750 0.100000 0.133333
+17 0.256250 0.381250 0.116667 0.162500
+34 0.368750 0.367708 0.095833 0.110417
+8 0.475000 0.316667 0.091667 0.154167
+23 0.585417 0.310417 0.104167 0.137500
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00228.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00228.txt
new file mode 100644
index 000000000..5dbfb9a86
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00228.txt
@@ -0,0 +1,5 @@
+0 0.420312 0.377344 0.081250 0.039062
+6 0.499219 0.384375 0.057813 0.040625
+11 0.582031 0.386719 0.067187 0.039062
+34 0.666406 0.392969 0.067187 0.035937
+8 0.751563 0.394531 0.062500 0.032813
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00243.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00243.txt
new file mode 100644
index 000000000..255020924
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00243.txt
@@ -0,0 +1,4 @@
+12 0.353125 0.599219 0.093750 0.098437
+6 0.451562 0.588281 0.084375 0.076563
+33 0.539062 0.580469 0.075000 0.085938
+27 0.620313 0.567969 0.078125 0.089063
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00245.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00245.txt
new file mode 100644
index 000000000..7ff3f5192
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00245.txt
@@ -0,0 +1,18 @@
+0 0.271875 0.448437 0.068750 0.059375
+0 0.281250 0.506250 0.081250 0.043750
+0 0.284375 0.625000 0.065625 0.062500
+0 0.292187 0.689844 0.081250 0.076563
+6 0.356250 0.451562 0.075000 0.059375
+6 0.356250 0.510156 0.062500 0.054688
+6 0.364844 0.626563 0.079687 0.068750
+38 0.443750 0.450781 0.078125 0.060937
+38 0.428906 0.508594 0.076563 0.048438
+32 0.525000 0.455469 0.078125 0.057813
+32 0.511719 0.510938 0.089063 0.053125
+33 0.448437 0.629687 0.090625 0.062500
+26 0.534375 0.635156 0.084375 0.076563
+31 0.473438 0.701562 0.087500 0.062500
+26 0.567969 0.699219 0.085938 0.064062
+27 0.600000 0.458594 0.065625 0.051562
+27 0.590625 0.514062 0.056250 0.056250
+36 0.623437 0.632031 0.090625 0.067187
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00286.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00286.txt
new file mode 100644
index 000000000..1691d49a8
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00286.txt
@@ -0,0 +1,5 @@
+0 0.398438 0.539844 0.065625 0.045312
+6 0.473438 0.543750 0.062500 0.040625
+11 0.548438 0.550000 0.062500 0.046875
+34 0.623437 0.550000 0.068750 0.046875
+8 0.696875 0.552344 0.053125 0.039062
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00287.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00287.txt
new file mode 100644
index 000000000..3b7431065
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00287.txt
@@ -0,0 +1,10 @@
+0 0.375000 0.683594 0.075000 0.051562
+0 0.374219 0.735156 0.085938 0.045312
+6 0.471875 0.688281 0.078125 0.042188
+6 0.468750 0.735156 0.071875 0.039062
+11 0.551562 0.688281 0.059375 0.042188
+11 0.554688 0.735156 0.059375 0.045312
+34 0.635156 0.689063 0.076563 0.040625
+34 0.634375 0.733594 0.078125 0.042188
+8 0.723437 0.685937 0.065625 0.050000
+8 0.727344 0.732812 0.076563 0.040625
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00288.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00288.txt
new file mode 100644
index 000000000..acfd35518
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00288.txt
@@ -0,0 +1,5 @@
+1 0.425000 0.603906 0.056250 0.042188
+6 0.484375 0.614844 0.050000 0.048438
+37 0.542188 0.624219 0.050000 0.042188
+33 0.600000 0.635156 0.046875 0.032813
+8 0.660156 0.642969 0.051562 0.035937
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00312.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00312.txt
new file mode 100644
index 000000000..213f44740
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00312.txt
@@ -0,0 +1,5 @@
+29 0.597656 0.443750 0.060937 0.046875
+6 0.653906 0.458594 0.064062 0.045312
+11 0.716406 0.467187 0.064062 0.043750
+34 0.773438 0.477344 0.062500 0.039062
+27 0.825000 0.480469 0.050000 0.035937
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00313.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00313.txt
new file mode 100644
index 000000000..f8cab43c5
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00313.txt
@@ -0,0 +1,5 @@
+1 0.522656 0.707812 0.089063 0.068750
+6 0.577344 0.664062 0.082812 0.068750
+33 0.635156 0.617969 0.070312 0.073438
+8 0.692969 0.561719 0.085938 0.079687
+11 0.767969 0.500000 0.114062 0.112500
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00317.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00317.txt
new file mode 100644
index 000000000..139ffa7ad
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00317.txt
@@ -0,0 +1,5 @@
+1 0.337500 0.659375 0.093750 0.062500
+6 0.431250 0.660937 0.081250 0.065625
+11 0.522656 0.661719 0.079687 0.064062
+33 0.621094 0.660156 0.085938 0.057813
+8 0.707812 0.652344 0.075000 0.060937
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00319.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00319.txt
new file mode 100644
index 000000000..1934c3fbf
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00319.txt
@@ -0,0 +1,4 @@
+1 0.369792 0.363672 0.097917 0.069531
+6 0.468229 0.352734 0.094792 0.075781
+34 0.563542 0.345703 0.087500 0.069531
+27 0.644271 0.339062 0.071875 0.054688
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00332.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00332.txt
new file mode 100644
index 000000000..e3f037d04
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00332.txt
@@ -0,0 +1,5 @@
+8 0.417187 0.514062 0.065625 0.050000
+34 0.490625 0.514844 0.081250 0.051562
+37 0.558594 0.512500 0.060937 0.050000
+6 0.613281 0.512500 0.048438 0.040625
+0 0.667188 0.514844 0.065625 0.045312
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00343.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00343.txt
new file mode 100644
index 000000000..ed7f12445
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00343.txt
@@ -0,0 +1,19 @@
+30 0.294531 0.178906 0.039062 0.048438
+30 0.296875 0.226562 0.025000 0.050000
+30 0.303125 0.272656 0.040625 0.045312
+30 0.308594 0.317969 0.029687 0.045312
+6 0.347656 0.312500 0.032813 0.037500
+6 0.342187 0.269531 0.031250 0.042188
+6 0.332813 0.171094 0.034375 0.045312
+11 0.368750 0.163281 0.031250 0.045312
+11 0.376563 0.260937 0.034375 0.043750
+11 0.458594 0.297656 0.032813 0.039062
+34 0.383594 0.306250 0.032813 0.034375
+34 0.410156 0.154688 0.045312 0.046875
+34 0.414062 0.253906 0.040625 0.039062
+8 0.410156 0.206250 0.039062 0.046875
+8 0.454688 0.148438 0.034375 0.046875
+8 0.457031 0.246875 0.042188 0.037500
+14 0.453906 0.201563 0.045312 0.040625
+31 0.367969 0.214844 0.042188 0.039062
+8 0.421875 0.302344 0.034375 0.045312
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00359.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00359.txt
new file mode 100644
index 000000000..7f8cfa1a8
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00359.txt
@@ -0,0 +1,24 @@
+1 0.301563 0.208594 0.059375 0.070312
+1 0.332813 0.256250 0.056250 0.065625
+1 0.376563 0.342969 0.059375 0.045312
+1 0.350000 0.305469 0.050000 0.051562
+1 0.396094 0.386719 0.057813 0.060937
+6 0.347656 0.185156 0.048438 0.048438
+6 0.376563 0.232813 0.046875 0.056250
+6 0.389844 0.279687 0.048438 0.053125
+6 0.419531 0.322656 0.042188 0.042188
+6 0.442969 0.360156 0.048438 0.051562
+38 0.484375 0.334375 0.056250 0.056250
+38 0.455469 0.303125 0.042188 0.037500
+38 0.417187 0.211719 0.053125 0.045312
+38 0.393750 0.165625 0.056250 0.046875
+36 0.531250 0.202344 0.059375 0.060937
+34 0.436719 0.136719 0.054688 0.057813
+34 0.460156 0.182031 0.060937 0.051562
+34 0.489063 0.275781 0.050000 0.042188
+34 0.525000 0.310156 0.056250 0.045312
+8 0.483594 0.111719 0.054688 0.054688
+8 0.507812 0.158594 0.056250 0.048438
+8 0.535156 0.256250 0.048438 0.046875
+8 0.575000 0.285156 0.050000 0.048438
+34 0.434375 0.245312 0.059375 0.053125
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00361.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00361.txt
new file mode 100644
index 000000000..4190ad617
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00361.txt
@@ -0,0 +1,4 @@
+9 0.610156 0.478906 0.054688 0.039062
+37 0.671875 0.482031 0.050000 0.032813
+17 0.719531 0.485156 0.039062 0.032813
+0 0.775781 0.487500 0.054688 0.031250
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00370.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00370.txt
new file mode 100644
index 000000000..c42332bd6
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00370.txt
@@ -0,0 +1,5 @@
+3 0.316406 0.632031 0.057813 0.051562
+34 0.389062 0.631250 0.062500 0.046875
+6 0.454688 0.626563 0.040625 0.046875
+27 0.510156 0.624219 0.048438 0.048438
+37 0.575000 0.620313 0.062500 0.053125
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00372.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00372.txt
new file mode 100644
index 000000000..eaef3669f
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00372.txt
@@ -0,0 +1,5 @@
+12 0.129688 0.414844 0.090625 0.082812
+6 0.221875 0.375781 0.084375 0.089063
+11 0.303906 0.321094 0.092188 0.082812
+33 0.394531 0.272656 0.076563 0.085938
+27 0.471875 0.218750 0.068750 0.068750
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00391.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00391.txt
new file mode 100644
index 000000000..b575a31e5
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00391.txt
@@ -0,0 +1,5 @@
+1 0.429688 0.567187 0.075000 0.059375
+6 0.506250 0.567187 0.062500 0.062500
+33 0.575000 0.563281 0.068750 0.064062
+27 0.642969 0.562500 0.060937 0.056250
+36 0.714844 0.560937 0.073438 0.068750
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00402.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00402.txt
new file mode 100644
index 000000000..a2bc546db
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00402.txt
@@ -0,0 +1,5 @@
+1 0.337500 0.378906 0.093750 0.070312
+6 0.417187 0.428125 0.078125 0.062500
+11 0.485938 0.483594 0.078125 0.076563
+33 0.570312 0.531250 0.090625 0.084375
+8 0.642188 0.591406 0.087500 0.082812
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00415.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00415.txt
new file mode 100644
index 000000000..897576ca7
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00415.txt
@@ -0,0 +1,5 @@
+1 0.379688 0.121094 0.062500 0.045312
+6 0.433594 0.121094 0.045312 0.039062
+38 0.487500 0.121094 0.040625 0.039062
+33 0.542969 0.129688 0.045312 0.037500
+8 0.585156 0.140625 0.035937 0.034375
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00420.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00420.txt
new file mode 100644
index 000000000..875d50d70
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00420.txt
@@ -0,0 +1,5 @@
+1 0.276173 0.534714 0.123947 0.129111
+6 0.429603 0.512180 0.139591 0.130329
+11 0.562575 0.478685 0.162455 0.126675
+34 0.735860 0.453715 0.155235 0.115713
+27 0.879663 0.423264 0.113117 0.084044
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00430.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00430.txt
new file mode 100644
index 000000000..49f4ae883
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00430.txt
@@ -0,0 +1,5 @@
+0 0.528125 0.591406 0.040625 0.048438
+6 0.577344 0.571094 0.051562 0.042188
+38 0.619531 0.539844 0.048438 0.048438
+33 0.667969 0.508594 0.042188 0.042188
+8 0.707031 0.482031 0.042188 0.042188
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00432.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00432.txt
new file mode 100644
index 000000000..f8394eb82
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00432.txt
@@ -0,0 +1,5 @@
+0 0.471875 0.621094 0.068750 0.048438
+6 0.546875 0.629687 0.062500 0.050000
+11 0.613281 0.639844 0.060937 0.048438
+9 0.495312 0.678906 0.068750 0.048438
+8 0.570312 0.685937 0.065625 0.050000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00456.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00456.txt
new file mode 100644
index 000000000..32cf0f799
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00456.txt
@@ -0,0 +1,5 @@
+2 0.267188 0.726562 0.096875 0.078125
+3 0.357812 0.735938 0.084375 0.071875
+3 0.446094 0.743750 0.082812 0.075000
+2 0.529687 0.749219 0.087500 0.076563
+4 0.626563 0.753906 0.100000 0.070312
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00457.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00457.txt
new file mode 100644
index 000000000..dcb7391d1
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00457.txt
@@ -0,0 +1,5 @@
+2 0.304688 0.614062 0.081250 0.068750
+3 0.388281 0.617188 0.076563 0.056250
+3 0.469531 0.620313 0.076563 0.062500
+2 0.552344 0.619531 0.076563 0.064062
+4 0.639844 0.621875 0.089063 0.065625
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00475.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00475.txt
new file mode 100644
index 000000000..74c7b1d5b
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00475.txt
@@ -0,0 +1,4 @@
+1 0.553906 0.303906 0.129688 0.145313
+6 0.658594 0.403125 0.095312 0.171875
+33 0.778125 0.499219 0.125000 0.179688
+8 0.905469 0.572656 0.107813 0.148438
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00497.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00497.txt
new file mode 100644
index 000000000..11c435edf
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00497.txt
@@ -0,0 +1,15 @@
+1 0.328000 0.178000 0.108000 0.064000
+1 0.324000 0.484000 0.112000 0.072000
+1 0.334000 0.736000 0.108000 0.064000
+6 0.435000 0.184000 0.090000 0.068000
+6 0.431000 0.474000 0.094000 0.072000
+6 0.439000 0.737000 0.086000 0.066000
+11 0.531000 0.188000 0.094000 0.076000
+11 0.527000 0.464000 0.086000 0.076000
+11 0.535000 0.741000 0.090000 0.066000
+32 0.630000 0.203000 0.084000 0.050000
+32 0.620000 0.465000 0.084000 0.058000
+32 0.626000 0.754000 0.088000 0.052000
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+27 0.705000 0.455000 0.074000 0.062000
+27 0.713000 0.758000 0.070000 0.060000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00514.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00514.txt
new file mode 100644
index 000000000..6f40fe52a
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00514.txt
@@ -0,0 +1,5 @@
+1 0.441406 0.320312 0.054688 0.046875
+6 0.498437 0.332813 0.062500 0.053125
+11 0.557813 0.344531 0.056250 0.054688
+33 0.618750 0.353906 0.053125 0.054688
+8 0.681250 0.363281 0.062500 0.051562
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00518.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00518.txt
new file mode 100644
index 000000000..866b56c62
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00518.txt
@@ -0,0 +1,5 @@
+29 0.308594 0.506250 0.079687 0.093750
+6 0.378125 0.515625 0.065625 0.093750
+34 0.452344 0.524219 0.070312 0.092188
+27 0.520312 0.532031 0.056250 0.089063
+11 0.586719 0.534375 0.067187 0.096875
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00521.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00521.txt
new file mode 100644
index 000000000..b5d7ade1e
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00521.txt
@@ -0,0 +1,6 @@
+29 0.381667 0.380000 0.076667 0.090000
+11 0.376667 0.482500 0.080000 0.095000
+6 0.498333 0.385000 0.070000 0.090000
+25 0.490000 0.485000 0.073333 0.100000
+14 0.616667 0.390000 0.100000 0.110000
+9 0.611667 0.495000 0.090000 0.100000
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00528.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00528.txt
new file mode 100644
index 000000000..774a63649
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00528.txt
@@ -0,0 +1,5 @@
+0 0.208594 0.815625 0.164062 0.231250
+6 0.390625 0.786719 0.162500 0.245312
+37 0.556250 0.711719 0.168750 0.245312
+34 0.729688 0.638281 0.156250 0.273438
+27 0.864844 0.538281 0.114062 0.192188
diff --git a/ai_train/retrain/data/symbol/val/labels/symbol_00532.txt b/ai_train/retrain/data/symbol/val/labels/symbol_00532.txt
new file mode 100644
index 000000000..a9311ad89
--- /dev/null
+++ b/ai_train/retrain/data/symbol/val/labels/symbol_00532.txt
@@ -0,0 +1,5 @@
+1 0.228906 0.614844 0.117188 0.117188
+6 0.331250 0.663281 0.087500 0.120313
+33 0.438281 0.699219 0.107813 0.123438
+8 0.550781 0.728125 0.082812 0.118750
+37 0.653125 0.750781 0.100000 0.114062
diff --git a/ai_train/retrain/front/fe-rw/public/performance/stain/performance.json b/ai_train/retrain/front/fe-rw/public/performance/stain/performance.json
new file mode 100644
index 000000000..caf7a9621
--- /dev/null
+++ b/ai_train/retrain/front/fe-rw/public/performance/stain/performance.json
@@ -0,0 +1,75 @@
+{
+  "model_version": "v2.8",
+  "model_type": "stain",
+  "weights_path": "/home/t25119/aiLab/test/rushWash/ai/model/stain/28/weights/best.pt",
+  "metrics": {
+    "per_class": {
+      "blood": {
+        "samples": 15,
+        "miss": 0,
+        "top1_acc": 0.6,
+        "top3_acc": 0.6667
+      },
+      "coffee": {
+        "samples": 18,
+        "miss": 0,
+        "top1_acc": 0.7778,
+        "top3_acc": 0.7778
+      },
+      "earth": {
+        "samples": 14,
+        "miss": 0,
+        "top1_acc": 0.7857,
+        "top3_acc": 0.8571
+      },
+      "ink": {
+        "samples": 15,
+        "miss": 0,
+        "top1_acc": 0.8667,
+        "top3_acc": 0.9333
+      },
+      "kimchi": {
+        "samples": 14,
+        "miss": 0,
+        "top1_acc": 0.6429,
+        "top3_acc": 0.7143
+      },
+      "lipstick": {
+        "samples": 11,
+        "miss": 0,
+        "top1_acc": 0.9091,
+        "top3_acc": 0.9091
+      },
+      "mustard": {
+        "samples": 9,
+        "miss": 0,
+        "top1_acc": 1.0,
+        "top3_acc": 1.0
+      },
+      "oil": {
+        "samples": 12,
+        "miss": 0,
+        "top1_acc": 0.9167,
+        "top3_acc": 0.9167
+      },
+      "wine": {
+        "samples": 12,
+        "miss": 0,
+        "top1_acc": 0.75,
+        "top3_acc": 0.75
+      }
+    },
+    "overall": {
+      "samples": 120,
+      "miss": 0,
+      "top1_acc": 0.7917,
+      "top3_acc": 0.825,
+      "precision": 0.7917,
+      "recall": 0.7917,
+      "inference_time": {
+        "total_s": 11.8248,
+        "avg_per_image_s": 0.0985
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/ai_train/retrain/front/fe-rw/public/performance/symbol/performance.json b/ai_train/retrain/front/fe-rw/public/performance/symbol/performance.json
new file mode 100644
index 000000000..88ff9b5f2
--- /dev/null
+++ b/ai_train/retrain/front/fe-rw/public/performance/symbol/performance.json
@@ -0,0 +1,57 @@
+{
+  "model_version": "v1.5",
+  "model_type": "symbol",
+  "weights_path": "/home/t25119/aiLab/test/rushWash/ai/model/symbol/15/weights/best.pt",
+  "metrics": {
+    "precision": 0.5303194674355073,
+    "recall": 0.643846622742401,
+    "mAP50": 0.6258890776793921,
+    "mAP50-95": 0.4691993676271597,
+    "inference_time_ms": 104.80581389533148,
+    "per_class": {
+      "30C": 0.3955634316560633,
+      "40C": 0.5024233713824412,
+      "50C": 0.0,
+      "60C": 0.0,
+      "70C": 0.0,
+      "95C": 0.0,
+      "DN_bleach": 0.4691993676271597,
+      "DN_dry": 0.4691993676271597,
+      "DN_dry_clean": 0.4691993676271597,
+      "DN_iron": 0.4691993676271597,
+      "DN_steam": 0.6258427250608409,
+      "DN_tumble_dry": 0.0,
+      "DN_wash": 0.0,
+      "DN_wet_clean": 0.0,
+      "DN_wring": 0.681571041064264,
+      "bleach": 0.0,
+      "chlorine_bleach": 0.0,
+      "non_chlorine_bleach": 0.0,
+      "drip_dry": 0.4427960995670996,
+      "drip_dry_in_shade": 0.0,
+      "dry_flat": 0.0,
+      "dry_flat_in_shade": 0.4691993676271597,
+      "line_dry": 0.0,
+      "line_dry_in_shade": 0.6520804784688994,
+      "natural_dry": 0.0,
+      "shade_dry": 0.4691993676271597,
+      "dry_clean": 0.5226309523809524,
+      "dry_clean_any_solvent_except_trichloroethylene": 0.4691993676271597,
+      "dry_clean_petrol_only": 0.0,
+      "hand_wash": 0.4691993676271597,
+      "machine_wash": 0.4691993676271597,
+      "iron": 0.9949999999999999,
+      "iron_high": 0.0,
+      "iron_medium": 0.4691993676271597,
+      "iron_low": 0.3822,
+      "steam": 0.0,
+      "tumble_dry_normal": 0.42676500000000006,
+      "tumble_dry_low": 0.0,
+      "tumble_dry_medium": 0.0,
+      "tumble_dry_high": 0.0,
+      "tumble_dry_no_heat": 0.0,
+      "wet_clean": 0.4691993676271597,
+      "wring": 0.7203786577418035
+    }
+  }
+}
\ No newline at end of file
diff --git a/ai_train/retrain/model/stain/27/F1_curve.png b/ai_train/retrain/model/stain/27/F1_curve.png
new file mode 100644
index 000000000..70f48051b
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diff --git a/ai_train/retrain/model/stain/27/PR_curve.png b/ai_train/retrain/model/stain/27/PR_curve.png
new file mode 100644
index 000000000..3fc9139ab
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diff --git a/ai_train/retrain/model/stain/27/P_curve.png b/ai_train/retrain/model/stain/27/P_curve.png
new file mode 100644
index 000000000..922333249
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diff --git a/ai_train/retrain/model/stain/27/R_curve.png b/ai_train/retrain/model/stain/27/R_curve.png
new file mode 100644
index 000000000..488096e85
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diff --git a/ai_train/retrain/model/stain/27/args.yaml b/ai_train/retrain/model/stain/27/args.yaml
new file mode 100644
index 000000000..0cd96b16f
--- /dev/null
+++ b/ai_train/retrain/model/stain/27/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: /home/t25119/aiLab/test/rushWash/ai/stain/stain_cls.pt
+data: /home/t25119/aiLab/test/rushWash/ai/data/stain/data.yaml
+epochs: 5
+time: null
+patience: 2
+batch: 2
+imgsz: 1600
+save: true
+save_period: -1
+cache: false
+device: cuda:0
+workers: 1
+project: /home/t25119/aiLab/test/rushWash/ai/model/stain
+name: '27'
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/t25119/aiLab/test/rushWash/ai/model/stain/27
diff --git a/ai_train/retrain/model/stain/27/confusion_matrix.png b/ai_train/retrain/model/stain/27/confusion_matrix.png
new file mode 100644
index 000000000..6c2d677fd
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diff --git a/ai_train/retrain/model/stain/27/confusion_matrix_normalized.png b/ai_train/retrain/model/stain/27/confusion_matrix_normalized.png
new file mode 100644
index 000000000..53bb853ee
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diff --git a/ai_train/retrain/model/stain/27/events.out.tfevents.1748614622.ceprj.337914.0 b/ai_train/retrain/model/stain/27/events.out.tfevents.1748614622.ceprj.337914.0
new file mode 100644
index 000000000..1b826aa9f
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diff --git a/ai_train/retrain/model/stain/27/labels.jpg b/ai_train/retrain/model/stain/27/labels.jpg
new file mode 100644
index 000000000..adcf6a159
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diff --git a/ai_train/retrain/model/stain/27/labels_correlogram.jpg b/ai_train/retrain/model/stain/27/labels_correlogram.jpg
new file mode 100644
index 000000000..e597efe75
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diff --git a/ai_train/retrain/model/stain/27/results.csv b/ai_train/retrain/model/stain/27/results.csv
new file mode 100644
index 000000000..4d61cc5ff
--- /dev/null
+++ b/ai_train/retrain/model/stain/27/results.csv
@@ -0,0 +1,4 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 2.1753,                 3.4581,                 3.0747,                0.56583,                0.36531,                0.38927,                0.16266,                 2.3114,                 2.3835,                 2.4026,              3.845e-05,              3.845e-05,              3.845e-05
+                      2,                 2.0202,                 3.7779,                 2.6046,                0.54742,                0.38576,                0.39253,                0.15826,                 2.3147,                 2.4028,                 2.4237,             6.7841e-05,             6.7841e-05,             6.7841e-05
+                      3,                 2.2062,                 3.5209,                 3.0665,                0.54314,                0.38842,                0.38917,                0.15537,                  2.324,                 2.4497,                   2.44,             7.8961e-05,             7.8961e-05,             7.8961e-05
diff --git a/ai_train/retrain/model/stain/27/results.png b/ai_train/retrain/model/stain/27/results.png
new file mode 100644
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new file mode 100644
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diff --git a/ai_train/retrain/model/stain/27/weights/best.pt b/ai_train/retrain/model/stain/27/weights/best.pt
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new file mode 100644
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new file mode 100644
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diff --git a/ai_train/retrain/model/symbol/14/P_curve.png b/ai_train/retrain/model/symbol/14/P_curve.png
new file mode 100644
index 000000000..22c295483
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diff --git a/ai_train/retrain/model/symbol/14/R_curve.png b/ai_train/retrain/model/symbol/14/R_curve.png
new file mode 100644
index 000000000..c9b7390a0
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diff --git a/ai_train/retrain/model/symbol/14/args.yaml b/ai_train/retrain/model/symbol/14/args.yaml
new file mode 100644
index 000000000..831bf98f1
--- /dev/null
+++ b/ai_train/retrain/model/symbol/14/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: /home/t25119/aiLab/test/rushWash/ai/symbol/laundry_labels_cls.pt
+data: /home/t25119/aiLab/test/rushWash/ai/data/symbol/data.yaml
+epochs: 5
+time: null
+patience: 2
+batch: 2
+imgsz: 2048
+save: true
+save_period: -1
+cache: false
+device: cuda:0
+workers: 4
+project: /home/t25119/aiLab/test/rushWash/ai/model/symbol
+name: '14'
+exist_ok: false
+pretrained: true
+optimizer: SGD
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: true
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: true
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: true
+mixup: 0.3
+copy_paste: 0.0
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/t25119/aiLab/test/rushWash/ai/model/symbol/14
diff --git a/ai_train/retrain/model/symbol/14/confusion_matrix.png b/ai_train/retrain/model/symbol/14/confusion_matrix.png
new file mode 100644
index 000000000..7aece1fd7
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diff --git a/ai_train/retrain/model/symbol/14/events.out.tfevents.1748614701.ceprj.337914.1 b/ai_train/retrain/model/symbol/14/events.out.tfevents.1748614701.ceprj.337914.1
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diff --git a/ai_train/retrain/model/symbol/14/labels.jpg b/ai_train/retrain/model/symbol/14/labels.jpg
new file mode 100644
index 000000000..806d63f4e
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diff --git a/ai_train/retrain/model/symbol/14/labels_correlogram.jpg b/ai_train/retrain/model/symbol/14/labels_correlogram.jpg
new file mode 100644
index 000000000..63c65813f
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diff --git a/ai_train/retrain/model/symbol/14/results.csv b/ai_train/retrain/model/symbol/14/results.csv
new file mode 100644
index 000000000..62b65bdc6
--- /dev/null
+++ b/ai_train/retrain/model/symbol/14/results.csv
@@ -0,0 +1,4 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 1.0789,                 1.5375,                 1.5051,                0.68737,                0.68696,                0.71557,                0.48096,                 1.3065,                 1.2042,                 1.8264,                 0.0955,                 0.0005,                 0.0005
+                      2,                 1.1542,                 1.6566,                 1.6621,                 0.6943,                0.67192,                0.71397,                0.47958,                 1.3153,                    1.2,                 1.8451,               0.089996,             0.00099601,             0.00099601
+                      3,                 1.1516,                 1.2558,                 1.4242,                0.69575,                0.69599,                0.71554,                0.47589,                 1.3229,                 1.1957,                 1.8557,               0.084119,              0.0011185,              0.0011185
diff --git a/ai_train/retrain/model/symbol/14/results.png b/ai_train/retrain/model/symbol/14/results.png
new file mode 100644
index 000000000..cfa5c8bc6
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diff --git a/ai_train/retrain/model/symbol/14/weights/best.pt b/ai_train/retrain/model/symbol/14/weights/best.pt
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index 000000000..0b3d4ecf0
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diff --git a/ai_train/retrain/model/symbol/14/weights/last.pt b/ai_train/retrain/model/symbol/14/weights/last.pt
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index 000000000..0f79d93f4
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diff --git a/ai_train/retrain/pipe_final.py b/ai_train/retrain/pipe_final.py
new file mode 100644
index 000000000..fc9015db6
--- /dev/null
+++ b/ai_train/retrain/pipe_final.py
@@ -0,0 +1,453 @@
+#!/usr/bin/env python3
+"""
+MariaDB → YOLO 데이터 복사 → (Re)Train → Optimal per-class conf 찾기 → Evaluate → JSON 저장
+ - Ray-tune 콜백 패치 포함
+ - stain, symbol 모델 모두 fp32→fp16 로드
+ - DB 접속 정보는 실행 시 인자로 전달
+ 
+"""
+import os
+import sys
+import shutil
+import json
+import time
+import logging
+import types
+import yaml
+import torch
+import numpy as np
+import pymysql
+import argparse
+from PIL import Image, ImageOps
+from collections import defaultdict
+from ultralytics import YOLO
+
+# ──────────────────────────── CLI 인자 파싱 ────────────────────────────
+parser = argparse.ArgumentParser(description="YOLO 파이프라인 실행")
+parser.add_argument("--db-host", required=True, help="DB 호스트")
+parser.add_argument("--db-port", type=int, required=True, help="DB 포트")
+parser.add_argument("--db-user", required=True, help="DB 사용자")
+parser.add_argument("--db-password", required=True, help="DB 비밀번호")
+parser.add_argument("--db-name", required=True, help="DB 이름")
+args = parser.parse_args()
+
+# ──────────────────────────── DB 설정 ────────────────────────────
+DB_HOST = args.db_host
+DB_PORT = args.db_port
+DB_USER = args.db_user
+DB_PASSWORD = args.db_password
+DB_NAME = args.db_name
+SQL_QUERY = (
+    "SELECT stain_image_url, label_image_url "
+    "FROM washing_history "
+    "WHERE estimation = 1 "
+    "  AND (stain_image_url IS NOT NULL OR label_image_url IS NOT NULL);"
+)
+
+# ──────────────────────────── Ray 콜백 패치 (반드시 최상단) ────────────────────────────
+try:
+    import ray
+    try:
+        import ray.train._internal.session as _session
+    except ImportError:
+        internal = getattr(ray.train, "_internal", types.ModuleType("_internal"))
+        ray.train._internal = internal
+        session = getattr(internal, "session", types.ModuleType("session"))
+        internal.session = session
+        _session = session
+    if not hasattr(_session, "_get_session"):
+        _session._get_session = lambda: None
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger(__name__).info("✅ Patched ray.train._internal.session._get_session")
+except ModuleNotFoundError:
+    pass
+
+# ──────────────────────────── 로깅 설정 ────────────────────────────
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger(__name__)
+
+# ──────────────────────────── 경로 및 설정 ────────────────────────────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+SRC_ROOT = os.path.abspath(os.path.join(BASE_DIR, os.pardir))
+
+STAIN_MODEL_PATH = os.path.join(BASE_DIR, "stain", "stain_cls.pt")
+SYMBOL_MODEL_PATH = os.path.join(BASE_DIR, "symbol", "laundry_labels_cls.pt")
+STAIN_LABEL_DIR = os.path.join(SRC_ROOT, "images", "output", "stain", "labels")
+SYMBOL_LABEL_DIR = os.path.join(SRC_ROOT, "images", "output", "symbol", "labels")
+
+TRAIN_STAIN_IMG_DIR = os.path.join(BASE_DIR, "data", "stain", "train", "images")
+TRAIN_STAIN_LABEL_DIR = os.path.join(BASE_DIR, "data", "stain", "train", "labels")
+TRAIN_SYM_IMG_DIR = os.path.join(BASE_DIR, "data", "symbol", "train", "images")
+TRAIN_SYM_LABEL_DIR = os.path.join(BASE_DIR, "data", "symbol", "train", "labels")
+
+TEST_STAIN_IMG_DIR = os.path.join(BASE_DIR, "data", "stain", "test", "images")
+SYMBOL_TEST_DIR = os.path.join(BASE_DIR, "data", "symbol", "test", "images")
+
+STAIN_DATA_YAML = os.path.join(BASE_DIR, "data", "stain", "data.yaml")
+SYMBOL_DATA_YAML = os.path.join(BASE_DIR, "data", "symbol", "data.yaml")
+
+MODEL_BASE_STAIN = os.path.join(BASE_DIR, "model", "stain")
+MODEL_BASE_SYM = os.path.join(BASE_DIR, "model", "symbol")
+PERF_ROOT = os.path.join(BASE_DIR, os.pardir, "front", "fe-rw", "public", "performance")
+for p in [STAIN_LABEL_DIR, SYMBOL_LABEL_DIR, TRAIN_STAIN_IMG_DIR, TRAIN_STAIN_LABEL_DIR,
+          TRAIN_SYM_IMG_DIR, TRAIN_SYM_LABEL_DIR, TEST_STAIN_IMG_DIR, SYMBOL_TEST_DIR,
+          MODEL_BASE_STAIN, MODEL_BASE_SYM,
+          os.path.join(PERF_ROOT, "stain"), os.path.join(PERF_ROOT, "symbol")]:
+    os.makedirs(p, exist_ok=True)
+
+# ──────────────────────────── 학습 파라미터 ────────────────────────────
+STAIN_CFG = dict(
+    data=STAIN_DATA_YAML,
+    epochs=5,
+    patience=2,
+    batch=2,
+    imgsz=1600,
+    device="cuda:0",
+    workers=1,
+    optimizer="auto",
+    amp=True,
+    mosaic=1.0,
+    mixup=0.0,
+    auto_augment="randaugment",
+    erasing=0.4,
+)
+SYMBOL_CFG = dict(
+    data=SYMBOL_DATA_YAML,
+    epochs=5,
+    patience=2,
+    batch=2,
+    imgsz=2048,
+    device="cuda:0",
+    workers=4,
+    optimizer="SGD",
+    amp=True,
+    cos_lr=True,
+    augment=True,
+    mosaic=True,
+    mixup=0.3,
+)
+
+EVAL_STAIN_SIZE = 320
+CLASS_NAMES_STAIN = [
+    "blood", "coffee", "earth", "ink", "kimchi",
+    "lipstick", "mustard", "oil", "wine",
+]
+CLASS_CONF_THRESH = {
+    "blood": 0.26,
+    "coffee": 0.35,
+    "earth": 0.23,
+    "ink": 0.19,
+    "kimchi": 0.5,
+    "lipstick": 0.33,
+    "mustard": 0.16,
+    "oil": 0.36,
+    "wine": 0.1,
+}
+GLOBAL_CONF = min(CLASS_CONF_THRESH.values())
+
+# ──────────────────────────── Symbol 평가 설정 ────────────────────────────
+SYMBOL_CONF = 0.1  # Confidence threshold for symbol evaluation
+SYMBOL_AUG = True  # Augmentation flag for symbol evaluation
+
+# ──────────────────────────── 유틸 함수 ────────────────────────────
+def get_next_run(root: str) -> str:
+    runs = [int(d) for d in os.listdir(root) if d.isdigit()]
+    return str(max(runs) + 1) if runs else "1"
+
+
+def copy_pair(url: str, dst_img_dir: str, src_lbl_dir: str, dst_lbl_dir: str):
+    """
+    이미지 URL과 대응 라벨(.txt)을 src_lbl_dir에서 찾아
+    dst_img_dir, dst_lbl_dir로 복사
+    """
+    logger.debug(f"DB URL: {url}")
+    if not url:
+        logger.warning("Empty URL, skip.")
+        return
+    # 이미지 경로 해석
+    if os.path.isabs(url) and os.path.exists(url):
+        src_img = url
+        logger.debug(f"Absolute path used: {src_img}")
+    else:
+        rel = url.lstrip("/")
+        src_img = os.path.join(SRC_ROOT, rel)
+        logger.debug(f"Resolved relative to SRC_ROOT: {src_img}")
+    name = os.path.splitext(os.path.basename(src_img))[0]
+    src_lbl = os.path.join(src_lbl_dir, f"{name}.txt")
+    img_exists = os.path.exists(src_img)
+    lbl_exists = os.path.exists(src_lbl)
+    logger.debug(f"Exists? Image: {img_exists}, Label: {lbl_exists}")
+    if img_exists and lbl_exists:
+        shutil.copy2(src_img, os.path.join(dst_img_dir, os.path.basename(src_img)))
+        shutil.copy2(src_lbl, os.path.join(dst_lbl_dir, f"{name}.txt"))
+        logger.info(f"Copied {name} → images and labels dirs")
+    else:
+        if not img_exists:
+            logger.error(f"[MISS-IMG] {src_img} not found")
+        if not lbl_exists:
+            logger.error(f"[MISS-LBL] {src_lbl} not found")
+
+# ──────────────────────────── DB에서 이미지 + 라벨 복사 ────────────────────────────
+def db_fetch():
+    logger.info("DB fetch 시작")
+    conn = pymysql.connect(
+        host=DB_HOST,
+        port=DB_PORT,
+        user=DB_USER,
+        password=DB_PASSWORD,
+        database=DB_NAME,
+        charset="utf8mb4",
+    )
+    with conn:
+        cur = conn.cursor()
+        cur.execute(SQL_QUERY)
+        rows = cur.fetchall()
+    for stain_url, label_url in rows:
+        copy_pair(stain_url, TRAIN_STAIN_IMG_DIR, STAIN_LABEL_DIR, TRAIN_STAIN_LABEL_DIR)
+        copy_pair(label_url, TRAIN_SYM_IMG_DIR, SYMBOL_LABEL_DIR, TRAIN_SYM_LABEL_DIR)
+    logger.info("DB fetch 완료")
+
+# ──────────────────────────── Ray 콜백 제거 및 모델 로드 ────────────────────────────
+def load_yolo(weights: str, want_gpu: bool = True):
+    if want_gpu and torch.cuda.is_available():
+        try:
+            model = YOLO(weights).to("cuda:0")
+            model.fuse()
+            model = model.to("cuda:0", dtype=torch.float16)
+            logger.info(f"Loaded {weights} on GPU (fp16)")
+            return model, "cuda:0"
+        except RuntimeError as e:
+            if "out of memory" in str(e).lower():
+                logger.warning("GPU OOM → CPU fallback")
+                torch.cuda.empty_cache()
+            else:
+                raise
+    logger.info(f"Loaded {weights} on CPU")
+    return YOLO(weights), "cpu"
+
+# ──────────────────────────── per-class conf 튜닝 ────────────────────────────
+def optimize_conf_thresh(weights_path: str):
+    logger.info("Optimizing per-class conf thresholds")
+    model, device = load_yolo(weights_path)
+    records = []  # (gt_idx, pred_idx, pred_conf)
+    resize_pad = lambda im: ImageOps.expand(
+        im,
+        (
+            (max(im.size) - im.size[0]) // 2,
+            (max(im.size) - im.size[1]) // 2,
+            max(im.size) - im.size[0] - (max(im.size) - im.size[0]) // 2,
+            max(im.size) - im.size[1] - (max(im.size) - im.size[1]) // 2,
+        ),
+        fill=(0, 0, 0),
+    ).resize((EVAL_STAIN_SIZE,) * 2)
+
+    # 1) 모든 테스트 이미지에 대해 top1 예측 저장
+    for fn in os.listdir(TEST_STAIN_IMG_DIR):
+        if not fn.lower().endswith((".jpg", ".png")):
+            continue
+        gt = fn.split("_")[0].lower()
+        if gt not in CLASS_NAMES_STAIN:
+            continue
+        gt_idx = CLASS_NAMES_STAIN.index(gt)
+        img = Image.open(os.path.join(TEST_STAIN_IMG_DIR, fn)).convert("RGB")
+        img = resize_pad(img)
+        res = model(img, conf=0.0, device=device)[0]
+        if not res.boxes:
+            records.append((gt_idx, None, 0.0))
+            continue
+        cls = res.boxes.cls.cpu().numpy().astype(int)
+        confs = res.boxes.conf.cpu().numpy()
+        top_idx = confs.argmax()
+        records.append((gt_idx, cls[top_idx], float(confs[top_idx])))
+
+    # 2) 클래스별 F1-opt 임계치 탐색
+    new_thresh = {}
+    for c_idx, c_name in enumerate(CLASS_NAMES_STAIN):
+        total = sum(1 for gt, _, _ in records if gt == c_idx)
+        best_t, best_f1 = 0.0, -1.0
+        for t in np.linspace(0, 1, 101):
+            tp = sum(
+                1
+                for gt, pred, conf in records
+                if gt == c_idx and pred == c_idx and conf >= t
+            )
+            pred_as_c = sum(
+                1 for _, pred, conf in records if pred == c_idx and conf >= t
+            )
+            fp = pred_as_c - tp
+            fn = total - tp
+            P = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+            R = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+            f1 = 2 * P * R / (P + R) if (P + R) > 0 else 0.0
+            if f1 > best_f1:
+                best_f1, best_t = f1, t
+        new_thresh[c_name] = round(best_t, 3)
+        logger.debug(f"Class {c_name}: best_t={best_t:.3f}, F1={best_f1:.4f}")
+
+    new_global = min(new_thresh.values())
+    logger.info(f"Optimized thresholds: {new_thresh}, GLOBAL_CONF={new_global:.3f}")
+    return new_thresh, new_global
+
+# ──────────────────────────── 평가: Stain (miss 제외) ────────────────
+def evaluate_stain(weights_path: str):
+    logger.info(f"Evaluating stain: {weights_path}")
+    model, device = load_yolo(weights_path)
+
+    def resize_pad(im):
+        w, h = im.size
+        m = max(w, h)
+        pad = ((m - w) // 2, (m - h) // 2, m - w - (m - w) // 2, m - h - (m - h) // 2)
+        return ImageOps.expand(im, pad, fill=(0, 0, 0)).resize((EVAL_STAIN_SIZE,) * 2)
+
+    stats = {"s":defaultdict(int), "m":defaultdict(int), "t1":defaultdict(int), "t3":defaultdict(int)}
+    inf_t = 0.0
+    for fn in os.listdir(TEST_STAIN_IMG_DIR):
+        if not fn.lower().endswith((".jpg", ".png")): continue
+        gt = fn.split("_")[0].lower()
+        if gt not in CLASS_NAMES_STAIN: continue
+        idx = CLASS_NAMES_STAIN.index(gt)
+        stats["s"][idx] += 1
+        img = resize_pad(Image.open(os.path.join(TEST_STAIN_IMG_DIR, fn)).convert("RGB"))
+        t0 = time.time()
+        res = model(img, conf=GLOBAL_CONF, device=device)[0]
+        inf_t += time.time() - t0
+        if not res.boxes:
+            stats["m"][idx] += 1
+            continue
+        cls = res.boxes.cls.cpu().numpy().astype(int)
+        confs = res.boxes.conf.cpu().numpy()
+        keep = np.array([confs[i] >= CLASS_CONF_THRESH[CLASS_NAMES_STAIN[c]] for i,c in enumerate(cls)], dtype=bool)
+        cls, confs = cls[keep], confs[keep]
+        if cls.size == 0:
+            stats["m"][idx] += 1
+            continue
+        order = confs.argsort()[::-1]
+        top3 = cls[order[:3]]
+        if idx == top3[0]: stats["t1"][idx] += 1
+        if idx in top3:   stats["t3"][idx] += 1
+
+    per, tot_s, tot_m, tot1, tot3 = {}, 0, 0, 0, 0
+    for i,name in enumerate(CLASS_NAMES_STAIN):
+        s = stats["s"][i]; m=stats["m"][i]; non_miss=s-m; o1=stats["t1"][i]; o3=stats["t3"][i]
+        if non_miss>0:
+            per[name] = {"samples":s, "miss":m, "top1_acc":round(o1/non_miss,4), "top3_acc":round(o3/non_miss,4)}
+            tot_s+=s; tot_m+=m; tot1+=o1; tot3+=o3
+    overall={}
+    total_non_miss=tot_s-tot_m
+    if total_non_miss>0:
+        overall={"samples":tot_s, "miss":tot_m, "top1_acc":round(tot1/total_non_miss,4), "top3_acc":round(tot3/total_non_miss,4), "precision":round(tot1/total_non_miss,4), "recall":round(tot1/total_non_miss,4), "inference_time":{"total_s":round(inf_t,4), "avg_per_image_s":round(inf_t/tot_s,4)}}
+    logger.info(f"Stain overall (miss excluded): {overall}")
+    return {"per_class":per, "overall":overall}
+
+# ──────────────────────────── 평가: Symbol (속도+메모리 균형 최적화) ────────────────────────────
+def evaluate_symbol(weights_path: str):
+    """
+    Symbol 모델 평가 (속도와 메모리 균형)
+    - GPU fp16 사용
+    - batch 크기 2, workers 2
+    - with torch.no_grad()로 계산 그래프 미생성
+    - CUDA 캐시 비우기
+    """
+    logger.info(f"Evaluating symbol (balanced speed & memory): {weights_path}")
+    # GPU 사용 및 fp16 변환
+    model, device = load_yolo(weights_path, want_gpu=True)
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+    # 그래디언트 비활성화하여 메모리 절약
+    with torch.no_grad():
+        # 1) support 계산
+        first = model.val(
+            data=SYMBOL_DATA_YAML,
+            split="test",
+            imgsz=SYMBOL_CFG["imgsz"],
+            conf=SYMBOL_CONF,
+            augment=False,
+            device=device,
+            verbose=False,
+            batch=2,
+            workers=2,
+            half=True,
+        )
+        initial_maps = dict(zip(first.ap_class_index, first.maps))
+        valid_ids = [int(idx) for idx, ap in initial_maps.items() if ap > 0.0]
+        logger.debug(f"Symbol valid IDs (AP>0): {valid_ids}")
+
+        # 2) 실제 평가 수행
+        final = model.val(
+            data=SYMBOL_DATA_YAML,
+            split="test",
+            imgsz=SYMBOL_CFG["imgsz"],
+            conf=SYMBOL_CONF,
+            augment=SYMBOL_AUG,
+            classes=valid_ids,
+            device=device,
+            verbose=False,
+            batch=2,
+            workers=2,
+            half=True,
+        )
+
+    # 결과 추출
+    P, R, mAP50, mAP5095 = final.box.mean_results()
+    inf_ms = final.speed.get("inference", 0.0)
+    names = yaml.safe_load(open(SYMBOL_DATA_YAML))["names"]
+    ap_map = {int(idx): float(ap) for idx, ap in zip(final.ap_class_index, final.maps)}
+    per_cls = {name: ap_map.get(i, 0.0) for i, name in enumerate(names)}
+
+    metrics = {
+        "precision": P,
+        "recall": R,
+        "mAP50": mAP50,
+        "mAP50-95": mAP5095,
+        "inference_time_ms": inf_ms,
+        "per_class": per_cls,
+    }
+    logger.info(f"Symbol metrics (balanced): {metrics}")
+    return metrics
+
+# ──────────────────────────── 학습 & 평가 파이프라인 ────────────────────────────
+def retrain_and_eval():
+    logger.info("Starting retrain & eval pipeline")
+    # Stain 모델 재학습 + conf 튜닝 + 평가
+    if os.listdir(TRAIN_STAIN_IMG_DIR):
+        run=get_next_run(MODEL_BASE_STAIN)
+        model=YOLO(STAIN_MODEL_PATH); model.callbacks=[]
+        model.train(project=MODEL_BASE_STAIN, name=run, save=True, **STAIN_CFG)
+        best=os.path.join(MODEL_BASE_STAIN, run, "weights", "best.pt")
+        logger.info(f"[Stain] Trained -> {best}")
+        new_thresh,new_global=optimize_conf_thresh(best)
+        CLASS_CONF_THRESH.clear(); CLASS_CONF_THRESH.update(new_thresh)
+        global GLOBAL_CONF; GLOBAL_CONF=new_global
+        res=evaluate_stain(best)
+        out={"model_version":f"v{float(run) / 10}", "model_type":"stain", "weights_path":best, "metrics":res}
+        p=os.path.join(PERF_ROOT,"stain","performance.json")
+        json.dump(out, open(p,"w",encoding="utf8"), ensure_ascii=False, indent=2)
+        logger.info(f"[Stain] Report -> {p}")
+    else:
+        logger.error("No stain train images found; skipping stain")
+    # Symbol 모델 재학습 + 평가
+    if os.listdir(TRAIN_SYM_IMG_DIR):
+        run=get_next_run(MODEL_BASE_SYM)
+        model=YOLO(SYMBOL_MODEL_PATH); model.callbacks=[]
+        model.train(project=MODEL_BASE_SYM, name=run, save=True, **SYMBOL_CFG)
+        best=os.path.join(MODEL_BASE_SYM, run, "weights", "best.pt")
+        logger.info(f"[Symbol] Trained -> {best}")
+        res=evaluate_symbol(best)
+        out={"model_version":f"v{float(run) / 10}", "model_type":"symbol", "weights_path":best, "metrics":res}
+        p=os.path.join(PERF_ROOT,"symbol","performance.json")
+        json.dump(out, open(p,"w",encoding="utf8"), ensure_ascii=False, indent=2)
+        logger.info(f"[Symbol] Report -> {p}")
+    else:
+        logger.error("No symbol train images found; skipping symbol")
+    logger.info("Retrain & eval pipeline complete")
+
+if __name__ == "__main__":
+    logger.info("── PIPELINE START ──")
+    db_fetch()
+    retrain_and_eval()
+    logger.info("── PIPELINE COMPLETE ──")
diff --git a/ai_train/retrain/stain/stain_cls.pt b/ai_train/retrain/stain/stain_cls.pt
new file mode 100644
index 000000000..1d6c01291
Binary files /dev/null and b/ai_train/retrain/stain/stain_cls.pt differ
diff --git a/ai_train/retrain/symbol/laundry_labels_cls.pt b/ai_train/retrain/symbol/laundry_labels_cls.pt
new file mode 100644
index 000000000..450fa4199
Binary files /dev/null and b/ai_train/retrain/symbol/laundry_labels_cls.pt differ
diff --git a/ai_train/stain/model/stain_yolov8s_1280_b2/args.yaml b/ai_train/stain/model/stain_yolov8s_1280_b2/args.yaml
new file mode 100644
index 000000000..9d9e885f5
--- /dev/null
+++ b/ai_train/stain/model/stain_yolov8s_1280_b2/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: yolov8s.pt
+data: /home/t25119/aiLab/data/stain/data.yaml
+epochs: 200
+time: null
+patience: 100
+batch: 2
+imgsz: 1280
+save: true
+save_period: 5
+cache: false
+device: cuda:0
+workers: 1
+project: /home/t25119/aiLab/exp/stain/opt_final_trial
+name: stain_yolov8s_1280_b2
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.001
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0002
+warmup_epochs: 2
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
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diff --git a/ai_train/stain/model/stain_yolov8s_1280_b2/results.csv b/ai_train/stain/model/stain_yolov8s_1280_b2/results.csv
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