inspecting yolo parameters

app.py

@@ -99,7 +99,11 @@ def predict_md(im,
     g = (size / max(im.size))  # multipl factor to make max size of the image equal to input size
     im = im.resize((int(x * g) for x in im.size), 
                     Image.ANTIALIAS)  # resize
-    MD_model = torch.hub.load('ultralytics/yolov5', 'custom', Megadet_Models[mega_model_input])
+    MD_model = torch.hub.load('ultralytics/yolov5', 
+                              'custom', 
+                              path = Megadet_Models[mega_model_input],
+                              force_reload = True,
+                              source='github')
     
     ## detect objects
     results = MD_model(im)  # inference # vars(results).keys()= dict_keys(['imgs', 'pred', 'names', 'files', 'times', 'xyxy', 'xywh', 'xyxyn', 'xywhn', 'n', 't', 's'])
@@ -132,6 +136,7 @@ def crop_animal_detections(yolo_results,
             pred_label = det_array[j,5]
 
             # keep animal crops above threshold
+            pdb.set_trace()
             if (pred_label == list_labels_as_str.index('animal')) and \
                 (pred_llk >= likelihood_th):
                 area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd)