the model is working. however, in need to generate it and split data in test and train beforehand ...

confic.py

@@ -1,9 +1,9 @@
 import torch
 import pathlib
 
-BATCH_SIZE = 32
+BATCH_SIZE = 8
 RESIZE_TO = 416
-NUM_EPOCHS = 20
+NUM_EPOCHS = 10
 NUM_WORKERS = 4
 
 DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

inference.py

@@ -10,6 +10,28 @@ from confic import NUM_CLASSES, DEVICE, CLASSES, OUTDIR
 
 from IPython import embed
 from tqdm.auto import tqdm
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+
+def show_sample(img_tensor, outputs, detection_threshold):
+
+    # embed()
+    # quit()
+    fig, ax = plt.subplots()
+    ax.imshow(img_tensor.squeeze().permute(1, 2, 0), aspect='auto')
+    for (x0, y0, x1, y1), l, score in zip(outputs[0]['boxes'].cpu(), outputs[0]['labels'].cpu(), outputs[0]['scores'].cpu()):
+
+        if score < detection_threshold:
+            continue
+    #     print(x0, y0, x1, y1, l)
+        ax.text(x0, y0, f'{score:.2f}', ha='left', va='bottom', fontsize=12, color='white')
+        ax.add_patch(
+            Rectangle((x0, y0),
+                      (x1 - x0),
+                      (y1 - y0),
+                      fill=False, color="white", linewidth=2, zorder=10)
+        )
+    plt.show()
 
 if __name__ == '__main__':
     model = create_model(num_classes=NUM_CLASSES)
@@ -18,6 +40,8 @@ if __name__ == '__main__':
     model.to(DEVICE).eval()
 
     DIR_TEST = 'data/train'
+
+
     test_images = glob.glob(f"{DIR_TEST}/*.png")
 
     detection_threshold = 0.8
@@ -35,3 +59,5 @@ if __name__ == '__main__':
             outputs = model(img_tensor.to(DEVICE))
 
         print(len(outputs[0]['boxes']))
+
+        show_sample(img_tensor, outputs, detection_threshold)

train.py

@@ -45,8 +45,6 @@ def validate(test_loader, model, val_loss):
 
         targets = [{k: v.to(DEVICE) for k, v in t.items()} for t in targets]
 
-        embed()
-        quit()
         with torch.inference_mode():
             loss_dict = model(images, targets)