Added model with augmentations and .json export metrics

data/models/README.md

@@ -9,6 +9,7 @@
 | ColonSegNet-07112023-2359.onnx (20MB or 20,067,028) | [:link:](TOADD) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1s-eKu6QjaV54jva_ylG1VxwCj1B8nwJ3)
 | Geometric Tx                                        | [:link:](https://drive.google.com/file/d/1vE023Thky0vKfoYD7wZecrglHEgp3z-W/view?usp=sharing) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1s-eKu6QjaV54jva_ylG1VxwCj1B8nwJ3) | 
 | Baseline model 4 epochs                             | [:link:](https://drive.google.com/file/d/1SWPzOeqKyKqBM9FVfj0qCPn8cxEhX9BK/view?usp=drive_link) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/TOADD) |
+| ColonSegNet_brightcontr.onnx (20MB) | [:link:](https://drive.google.com/file/d/1IoeDmH-nBcOrmrg1SBEx2wuMjRCuO0K_/view?usp=drive_link) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CgdpeDY4SLpKddk6dFvXpFwcx0skF8dj#scrollTo=e7dn13rCydBf) |
 | Add Your Model by creation a new row      | [:link:](COPY&PASTE_DRIVE_LINK) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/COPY&PASTE_DRIVE_LINK) |
 
 ### Examples to download models in your local machine

eval_metrics_brightcontr_model.json

@@ -0,0 +1,9 @@
+{
+    "Jaccard": 0.3948154304786141,
+    "F1": 0.5096914044004484,
+    "Recall": 0.6564222323237617,
+    "Precision": 0.5460967256637365,
+    "Acc": 0.8621585210164388,
+    "F2": 0.5096914044004484,
+    "Mean FPS": 0.1118420037964724
+}

eval_metrics_orig_model.json

@@ -0,0 +1,9 @@
+{
+    "Jaccard": 0.7238557526652066,
+    "F1": 0.8206050156575089,
+    "Recall": 0.8496177143081016,
+    "Precision": 0.8434546635722502,
+    "Acc": 0.9493032137552897,
+    "F2": 0.8206050156575089,
+    "Mean FPS": 0.10518452201876329
+}

export_eval_metrics.py

@@ -0,0 +1,25 @@
+print(f"Jaccard: {jaccard:1.4f} - F1: {f1:1.4f} - Recall: {recall:1.4f} - Precision: {precision:1.4f} - Acc: {acc:1.4f} - F2: {f2:1.4f}")
+print("Mean FPS: ", mean_fps)
+import json
+
+data = {
+    "Jaccard": jaccard,
+    "F1": f1,
+    "Recall": recall,
+    "Precision": precision,
+    "Acc": acc,
+    "F2": f2,
+    "Mean FPS": mean_fps,
+}
+
+# ct = datetime.datetime.now()
+# ts = ct.timestamp()
+# ts_str = str(ts)
+file_ext = ".json"
+path_to_file = "results/eval_metrics_brightcontr_model" #ts_str
+path_to_file = path_to_file + file_ext
+
+text = json.dumps(data, indent=4)
+with open(path_to_file, "w") as out_file_obj:
+    # write the text into the file
+    out_file_obj.write(text)

files/train_log.txt

@@ -0,0 +1,24 @@
+
+Dataset Size after Augmentation:
+Train: 1760 - Valid: 120
+
+Hyperparameters:
+Image Size: (512, 512)
+Batch Size: 6
+LR: 0.0001
+Epochs: 3
+Optimizer: Adam
+Loss: BCE Dice Loss
+
+Epoch: 01 | Epoch Time: 9m 48s
+	Train Loss: 1.088
+	 Val. Loss: 0.991
+
+Epoch: 02 | Epoch Time: 9m 47s
+	Train Loss: 0.888
+	 Val. Loss: 0.900
+
+Epoch: 03 | Epoch Time: 9m 47s
+	Train Loss: 0.780
+	 Val. Loss: 0.868
+

nvidia-clara-agx/colonoscopy_segmentation/colonoscopy_segmentation.py

@@ -84,6 +84,7 @@ def __init__(self, data, source="replayer"):
         self.sample_data_path = data
 
         self.model_path_map = {
+
             #"ultrasound_seg": os.path.join(self.sample_data_path, "colon.onnx"),
             "ultrasound_seg": os.path.join(self.sample_data_path, "ColonSegNet-07112023-2359.onnx"),
             #"ultrasound_seg": os.path.join(self.sample_data_path, "ColonSegNet_brightcontr.onnx"),