updates

train.py

@@ -65,8 +65,8 @@ def train():
     multi_scale = opt.multi_scale
 
     if multi_scale:
-        img_sz_min = round(img_size / 32 / 1.5) + 1
-        img_sz_max = round(img_size / 32 * 1.5) - 1
+        img_sz_min = round(img_size / 32 / 1.5)
+        img_sz_max = round(img_size / 32 * 1.5)
         img_size = img_sz_max * 32  # initiate with maximum multi_scale size
         print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
 
@@ -383,10 +383,15 @@ def train():
 def prebias():
     # trains output bias layers for 1 epoch and creates new backbone
     if opt.prebias:
+        a = opt.img_weights  # save settings
+        opt.img_weights = False  # disable settings
+
         train()  # transfer-learn yolo biases for 1 epoch
         create_backbone(last)  # saved results as backbone.pt
+
         opt.weights = wdir + 'backbone.pt'  # assign backbone
         opt.prebias = False  # disable prebias
+        opt.img_weights = a  # reset settings
 
 
 if __name__ == '__main__':
@@ -407,7 +412,7 @@ if __name__ == '__main__':
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
     parser.add_argument('--img-weights', action='store_true', help='select training images by weight')
     parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
-    parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='initial weights')
+    parser.add_argument('--weights', type=str, default='', help='initial weights')
     parser.add_argument('--arc', type=str, default='default', help='yolo architecture')  # defaultpw, uCE, uBCE
     parser.add_argument('--prebias', action='store_true', help='transfer-learn yolo biases prior to training')
     parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')