train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Thermal Face Detection"
      ],
      "metadata": {
        "id": "jgkQxmsWf0Kh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyYAML"
      ],
      "metadata": {
        "id": "rbXJ28PWRjYX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "460149de-13c1-41f5-c9ed-efd524dcae8f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (6.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# YoloV7"
      ],
      "metadata": {
        "id": "o-pnDFlZfDlE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHOnqsrZQ2ir",
        "outputId": "2ac68174-ae5e-4526-d97f-84ba0328a410"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov7'...\n",
            "remote: Enumerating objects: 1197, done.\u001b[K\n",
            "remote: Total 1197 (delta 0), reused 0 (delta 0), pack-reused 1197\u001b[K\n",
            "Receiving objects: 100% (1197/1197), 74.23 MiB | 27.50 MiB/s, done.\n",
            "Resolving deltas: 100% (519/519), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/WongKinYiu/yolov7"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "o_DZ3jZJcYIx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd yolov7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JARLSL66RBqH",
        "outputId": "4ae62691-5275-4192-c4d3-58f82d516a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download dataset from roboflow (https://universe.roboflow.com/eceproject/face-recognition-oev8w)"
      ],
      "metadata": {
        "id": "A-SPvtvSQ4Ef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -L \"https://universe.roboflow.com/ds/0XqZrmOy0m?key=xCbwgzjYyK\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "metadata": {
        "id": "q-5Kka8h0fYc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile data.yaml\n",
        "\n",
        "train: /content/yolov7/train/images/\n",
        "val: /content/yolov7/valid/images/\n",
        "\n",
        "nc: 1\n",
        "names: ['face']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpg7oNWkSKEw",
        "outputId": "b186fb85-2df8-496a-daad-47e2c2def856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting data.yaml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile  data/hyp.scratch.p5.yaml\n",
        "\n",
        "lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)\n",
        "lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)\n",
        "momentum: 0.937  # SGD momentum/Adam beta1\n",
        "weight_decay: 0.0005  # optimizer weight decay 5e-4\n",
        "warmup_epochs: 3.0  # warmup epochs (fractions ok)\n",
        "warmup_momentum: 0.8  # warmup initial momentum\n",
        "warmup_bias_lr: 0.1  # warmup initial bias lr\n",
        "box: 0.05  # box loss gain\n",
        "cls: 0.3  # cls loss gain\n",
        "cls_pw: 1.0  # cls BCELoss positive_weight\n",
        "obj: 0.7  # obj loss gain (scale with pixels)\n",
        "obj_pw: 1.0  # obj BCELoss positive_weight\n",
        "iou_t: 0.20  # IoU training threshold\n",
        "anchor_t: 4.0  # anchor-multiple threshold\n",
        "# anchors: 3  # anchors per output layer (0 to ignore)\n",
        "fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)\n",
        "hsv_h: 0.015  # image HSV-Hue augmentation (fraction)\n",
        "hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)\n",
        "hsv_v: 0.4  # image HSV-Value augmentation (fraction)\n",
        "degrees: 0.0  # image rotation (+/- deg)\n",
        "translate: 0.2  # image translation (+/- fraction)\n",
        "scale: 0.35  # image scale (+/- gain)\n",
        "shear: 0.0  # image shear (+/- deg)\n",
        "perspective: 0.0  # image perspective (+/- fraction), range 0-0.001\n",
        "flipud: 0.5  # image flip up-down (probability)\n",
        "fliplr: 0.5  # image flip left-right (probability)\n",
        "mosaic: 1.0  # image mosaic (probability)\n",
        "mixup: 0.25  # image mixup (probability)\n",
        "copy_paste: 0.0  # image copy paste (probability)\n",
        "paste_in: 0.3  # image copy paste (probability), use 0 for faster training\n",
        "loss_ota: 1 # use ComputeLossOTA, use 0 for faster training"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXPiqvwASkfG",
        "outputId": "b671ea3a-e756-4ec4-fff3-e643beb940de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting data/hyp.scratch.p5.yaml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download weights from https://github.com/WongKinYiu/yolov7"
      ],
      "metadata": {
        "id": "_hFrbTBVyLAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -L \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt\" > yolov7x_training.pt;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuD_S2pxIPzM",
        "outputId": "994a403e-aaf8-4830-d381-9b33aeeddf51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100 72.0M  100 72.0M    0     0  27.7M      0  0:00:02  0:00:02 --:--:-- 39.0M\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECVBtvRLDnUI",
        "outputId": "d179566f-9998-419f-d540-be0ce8ac3dc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Nov 19 15:55:35 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   44C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/yolov7/runs/train/yolov7"
      ],
      "metadata": {
        "id": "IX7xeM9YEedU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py --workers 1 --device 0 --batch-size 12 --single-cls --cache-images --data data.yaml --img 640 --cfg cfg/training/yolov7x.yaml --weights 'yolov7x_training.pt' --name yolov7 --hyp data/hyp.scratch.p5.yaml --epochs=40"
      ],
      "metadata": {
        "id": "ijUusRLkeYZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "aLWPLEmjyrg3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# YoloV9"
      ],
      "metadata": {
        "id": "KwsulYzJfHwM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !git clone https://github.com/WongKinYiu/yolov9"
      ],
      "metadata": {
        "id": "e34yqtS5carD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/SkalskiP/yolov9.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEs2o7meesmv",
        "outputId": "3920af4b-9d33-4bca-f9f8-231d04a47be0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov9'...\n",
            "remote: Enumerating objects: 325, done.\u001b[K\n",
            "remote: Counting objects: 100% (165/165), done.\u001b[K\n",
            "remote: Compressing objects: 100% (68/68), done.\u001b[K\n",
            "remote: Total 325 (delta 129), reused 97 (delta 97), pack-reused 160\u001b[K\n",
            "Receiving objects: 100% (325/325), 2.26 MiB | 9.42 MiB/s, done.\n",
            "Resolving deltas: 100% (159/159), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd yolov9"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOlYvSe8cc2O",
        "outputId": "21db0307-9cf7-4ae9-fa8e-62e09d555af2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download dataset from roboflow (https://universe.roboflow.com/eceproject/face-recognition-oev8w)\n",
        "!curl -L \"https://universe.roboflow.com/ds/0XqZrmOy0m?key=xCbwgzjYyK\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "metadata": {
        "id": "xFp5yVHaccz5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile data.yaml\n",
        "\n",
        "train: /content/yolov9/train/images/\n",
        "val: /content/yolov9/valid/images/\n",
        "\n",
        "nc: 1\n",
        "names: ['face']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEABvD8xccxh",
        "outputId": "db8ffbf5-01b6-406e-e194-2bc8a09acd0a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting data.yaml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXMi2vumcqVf",
        "outputId": "efd89734-2a31-4ba7-933c-0945427dd722"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Mar 15 14:21:37 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -L \"https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt\" > gelan-c.pt;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ms-hgGkqcqSo",
        "outputId": "f5efd65b-d3c6-4f7a-e5aa-f146f30e0882"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100 49.1M  100 49.1M    0     0  40.9M      0  0:00:01  0:00:01 --:--:-- 62.4M\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def change_first_char_to_zero(directory):\n",
        "    # List all files in the directory\n",
        "    files = os.listdir(directory)\n",
        "\n",
        "    # Loop through each file\n",
        "    for filename in files:\n",
        "        # Check if it's a file (not a directory)\n",
        "        if os.path.isfile(os.path.join(directory, filename)):\n",
        "            # Read the content of the file\n",
        "            with open(os.path.join(directory, filename), 'r') as file:\n",
        "                content = file.read()\n",
        "\n",
        "            # Modify the content by changing the first character to '0'\n",
        "            modified_content = '0' + content[1:]\n",
        "\n",
        "            # Write the modified content back to the file\n",
        "            with open(os.path.join(directory, filename), 'w') as file:\n",
        "                file.write(modified_content)\n",
        "                #print(f\"Changed first character of '{filename}' to '0'\")\n",
        "\n",
        "# Change the directory path to the one you want to operate on\n",
        "directory_path = '/content/yolov9/train/labels/'\n",
        "change_first_char_to_zero(directory_path)\n",
        "\n",
        "directory_path = '/content/yolov9/test/labels/'\n",
        "change_first_char_to_zero(directory_path)\n",
        "\n",
        "directory_path = '/content/yolov9/valid/labels/'\n",
        "change_first_char_to_zero(directory_path)"
      ],
      "metadata": {
        "id": "Qq1kjzKuhr2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py \\\n",
        "--batch 16 --epochs 25 --img 640 --device 0 --min-items 0 --close-mosaic 15 \\\n",
        "--data data.yaml \\\n",
        "--weights 'gelan-c.pt' \\\n",
        "--cfg models/detect/gelan-c.yaml \\\n",
        "--hyp hyp.scratch-high.yaml \\\n",
        "--single-cls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EARXm6NdSIx",
        "outputId": "f6eeff53-d7f2-4e2d-914f-b6ba673672e5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-03-15 14:48:37.130857: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-03-15 14:48:37.130915: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-03-15 14:48:37.132814: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-03-15 14:48:38.508097: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=gelan-c.pt, cfg=models/detect/gelan-c.yaml, data=data.yaml, hyp=hyp.scratch-high.yaml, epochs=25, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=True, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, flat_cos_lr=False, fixed_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, min_items=0, close_mosaic=15, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "YOLOv5 🚀 1e33dbb Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, 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, cls_pw=1.0, dfl=1.5, obj_pw=1.0, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n",
            "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLO 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLO 🚀 runs in Comet\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
            "  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  2                -1  1    212864  models.common.RepNCSPELAN4              [128, 256, 128, 64, 1]        \n",
            "  3                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
            "  4                -1  1    847616  models.common.RepNCSPELAN4              [256, 512, 256, 128, 1]       \n",
            "  5                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
            "  6                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
            "  7                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
            "  8                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
            "  9                -1  1    656896  models.common.SPPELAN                   [512, 512, 256]               \n",
            " 10                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 11           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 12                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
            " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 14           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 15                -1  1    912640  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 1]      \n",
            " 16                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
            " 17          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
            " 18                -1  1   2988544  models.common.RepNCSPELAN4              [768, 512, 512, 256, 1]       \n",
            " 19                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
            " 20           [-1, 9]  1         0  models.common.Concat                    [1]                           \n",
            " 21                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
            " 22      [15, 18, 21]  1   5491411  models.yolo.DDetect                     [1, [256, 512, 512]]          \n",
            "gelan-c summary: 621 layers, 25437843 parameters, 25437827 gradients\n",
            "\n",
            "Transferred 931/937 items from gelan-c.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 154 weight(decay=0.0), 161 weight(decay=0.0005), 160 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov9/train/labels.cache... 724 images, 0 backgrounds, 0 corrupt: 100% 724/724 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov9/valid/labels.cache... 206 images, 0 backgrounds, 0 corrupt: 100% 206/206 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/train/exp4/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp4\u001b[0m\n",
            "Starting training for 25 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       0/24      10.9G      2.382      3.549      2.088          5        640: 100% 46/46 [00:49<00:00,  1.07s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.41it/s]\n",
            "                   all        206        206      0.396       0.35      0.342      0.127\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       1/24      13.1G      1.922      1.948      1.596          9        640: 100% 46/46 [00:40<00:00,  1.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.34it/s]\n",
            "                   all        206        206      0.757      0.605      0.727      0.311\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       2/24      13.1G       1.87      1.839       1.53         11        640: 100% 46/46 [00:38<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.39it/s]\n",
            "                   all        206        206      0.303      0.485      0.313      0.107\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       3/24      13.1G       1.86      1.728      1.491          6        640: 100% 46/46 [00:38<00:00,  1.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.43it/s]\n",
            "                   all        206        206       0.83      0.712      0.778      0.332\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       4/24      13.1G      1.838      1.527      1.473          4        640: 100% 46/46 [00:38<00:00,  1.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.36it/s]\n",
            "                   all        206        206      0.757      0.728      0.788      0.346\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       5/24      13.1G      1.804      1.386      1.475          7        640: 100% 46/46 [00:38<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.42it/s]\n",
            "                   all        206        206      0.727      0.563       0.59      0.235\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       6/24      13.1G      1.816      1.336      1.498         10        640: 100% 46/46 [00:37<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.44it/s]\n",
            "                   all        206        206      0.805      0.864        0.9      0.409\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       7/24      13.1G      1.764      1.263       1.46         12        640: 100% 46/46 [00:37<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.37it/s]\n",
            "                   all        206        206      0.883       0.66      0.801      0.376\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       8/24      13.1G      1.788      1.223      1.483          7        640: 100% 46/46 [00:38<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.44it/s]\n",
            "                   all        206        206      0.837      0.806      0.846      0.354\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       9/24      13.1G      1.786      1.171      1.461          7        640: 100% 46/46 [00:38<00:00,  1.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.34it/s]\n",
            "                   all        206        206      0.827      0.908      0.915      0.459\n",
            "Closing dataloader mosaic\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      10/24      13.1G      1.682      1.171      1.547          4        640: 100% 46/46 [00:37<00:00,  1.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.44it/s]\n",
            "                   all        206        206      0.825      0.868      0.869      0.406\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      11/24      13.1G       1.68       1.08      1.549          4        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.33it/s]\n",
            "                   all        206        206       0.84      0.866      0.888      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      12/24      13.1G       1.67     0.9968      1.567          4        640: 100% 46/46 [00:37<00:00,  1.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.43it/s]\n",
            "                   all        206        206      0.898       0.85      0.924      0.441\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      13/24      13.1G      1.675     0.9678      1.538          4        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.31it/s]\n",
            "                   all        206        206      0.956      0.908      0.963      0.483\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      14/24      13.1G      1.676     0.9762      1.545          4        640: 100% 46/46 [00:38<00:00,  1.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.38it/s]\n",
            "                   all        206        206      0.858      0.853      0.901      0.453\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      15/24      13.1G      1.617     0.9591      1.548          3        640: 100% 46/46 [00:37<00:00,  1.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.43it/s]\n",
            "                   all        206        206      0.888      0.903      0.932      0.491\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      16/24      13.1G      1.628     0.9271       1.53          4        640: 100% 46/46 [00:37<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.35it/s]\n",
            "                   all        206        206      0.948      0.886      0.941      0.491\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      17/24      13.1G      1.605      0.891       1.52          4        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.41it/s]\n",
            "                   all        206        206      0.966      0.957      0.988      0.499\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      18/24      13.1G      1.567     0.8231      1.507          4        640: 100% 46/46 [00:37<00:00,  1.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.44it/s]\n",
            "                   all        206        206       0.97      0.942      0.976      0.497\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      19/24      13.1G      1.554      0.813      1.491          4        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.36it/s]\n",
            "                   all        206        206      0.982      0.961       0.99      0.541\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      20/24      13.1G       1.55     0.7975       1.46          4        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.44it/s]\n",
            "                   all        206        206      0.975      0.945      0.972      0.515\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      21/24      13.1G      1.533     0.7653      1.449          4        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.38it/s]\n",
            "                   all        206        206      0.973      0.971      0.984      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      22/24      13.1G      1.486     0.7395      1.408          3        640: 100% 46/46 [00:37<00:00,  1.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.45it/s]\n",
            "                   all        206        206      0.979      0.951      0.989      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      23/24      13.1G      1.504     0.7207      1.436          3        640: 100% 46/46 [00:37<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:05<00:00,  1.31it/s]\n",
            "                   all        206        206      0.975      0.966      0.993      0.574\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      24/24      13.1G      1.459     0.7005      1.382          4        640: 100% 46/46 [00:37<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:04<00:00,  1.40it/s]\n",
            "                   all        206        206      0.961      0.966      0.985       0.58\n",
            "\n",
            "25 epochs completed in 0.322 hours.\n",
            "Optimizer stripped from runs/train/exp4/weights/last.pt, saved as runs/train/exp4/weights/last_striped.pt, 51.5MB\n",
            "Optimizer stripped from runs/train/exp4/weights/best.pt, saved as runs/train/exp4/weights/best_striped.pt, 51.5MB\n",
            "\n",
            "Validating runs/train/exp4/weights/best.pt...\n",
            "Fusing layers... \n",
            "gelan-c summary: 467 layers, 25411731 parameters, 0 gradients\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 7/7 [00:07<00:00,  1.08s/it]\n",
            "                   all        206        206      0.961      0.966      0.985      0.579\n",
            "Results saved to \u001b[1mruns/train/exp4\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "6rBWQjXdcZMb"
      }
    }
  ]
}