diff --git a/train.py b/train.py
index efa92980..052aaf11 100644
--- a/train.py
+++ b/train.py
@@ -76,7 +76,7 @@ def train(
 
     if multi_scale:
         img_size = round((img_size / 32) * 1.5) * 32  # initiate with maximum multi_scale size
-        opt.num_workers = 0  # bug https://github.com/ultralytics/yolov3/issues/174
+        # opt.num_workers = 0  # bug https://github.com/ultralytics/yolov3/issues/174
     else:
         torch.backends.cudnn.benchmark = True  # unsuitable for multiscale
 
@@ -308,9 +308,9 @@ if __name__ == '__main__':
     parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
     parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing')
     parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
-    parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
+    parser.add_argument('--data-cfg', type=str, default='data/coco_32img.data', help='coco.data file path')
     parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
-    parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
+    parser.add_argument('--img-size', type=int, default=320, help='inference size (pixels)')
     parser.add_argument('--resume', action='store_true', help='resume training flag')
     parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
     parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')