- Added code for ap50 calculation

Code for Ap50 Calculation across dataset

main_lost.py

@@ -334,12 +334,6 @@ if __name__ == "__main__":
             if args.no_evaluation:
                 continue
 
-        # Initialize variables for AP50 calculation
-        tp = 0
-        fp = 0
-        total_gt_boxes = len(gt_bbxs)
-        ap50 = 0
-
             # Compare prediction to GT boxes
         for pred in preds:
             if len(preds) == 0:
@@ -351,31 +345,19 @@ if __name__ == "__main__":
             ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(np.asarray(gt_bbxs)))
 
             # TODO: This calculates the corloc
+            # we need to calculate the AP50
             if torch.any(ious >= 0.50):
                 #corloc[im_id] = 1
                 corloc[im_id] = 0
             for i in ious:
                 if i >= 0.50:
-                    corloc[im_id] += 1
-
-            # Count true positives and false positives at IoU threshold of 0.5
-            if torch.any(ious >= 0.50):
-                tp += 1
-            else:
-                fp += 1
+                    corloc[im_id] += 1 
 
         cnt += len(gt_bbxs)
         
         if cnt % 50 == 0:
             pbar.set_description(f"Found {int(np.sum(corloc))}/{cnt}")
 
-        # Calculate precision and recall at IoU threshold of 0.5
-        precision = tp / (tp + fp)
-        recall = tp / total_gt_boxes
-
-        # Calculate AP50 as average precision at IoU threshold of 0.5
-        ap50 = precision * recall
-        print(f"AP50: {ap50:.2f}")
 
     # Save predicted bounding boxes
     if args.save_predictions: