updates

detect.py

@@ -18,8 +18,8 @@
 
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
 parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
-parser.add_argument('-conf_thres', type=float, default=0.8, help='object confidence threshold')
-parser.add_argument('-nms_thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
+parser.add_argument('-conf_thres', type=float, default=0.99, help='object confidence threshold')
+parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
 parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
 parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
 opt = parser.parse_args()
@@ -33,7 +33,8 @@ def detect(opt):
     # Load model
     model = Darknet(opt.cfg, opt.img_size)
 
-    weights_path = 'checkpoints/yolov3.weights'
+    #weights_path = 'checkpoints/yolov3.weights'
+    weights_path = 'checkpoints/latest.pt'
     if weights_path.endswith('.weights'):  # saved in darknet format
         load_weights(model, weights_path)
     else:  # endswith('.pt'), saved in pytorch format
@@ -130,7 +131,7 @@ def detect(opt):
 
                 if opt.plot_flag:
                     # Add the bbox to the plot
-                    label = '%s %.2f' % (classes[int(cls_pred)], cls_conf) if cls_conf > 0.05 else None
+                    label = '%s %.2f' % (classes[int(cls_pred)], conf)
                     color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
                     plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3)