diff --git a/train.py b/train.py
index 8c8e5b15..bc2a3e3b 100644
--- a/train.py
+++ b/train.py
@@ -64,9 +64,9 @@ def train(
         torch.backends.cudnn.benchmark = True  # unsuitable for multiscale
 
     # Configure run
-    data_cfg = parse_data_cfg(data_cfg)
-    train_path = data_cfg['train']
-    nc = int(data_cfg['classes'])  # number of classes
+    data_dict = parse_data_cfg(data_cfg)
+    train_path = data_dict['train']
+    nc = int(data_dict['classes'])  # number of classes
 
     # Initialize model
     model = Darknet(cfg, img_size).to(device)
@@ -276,12 +276,12 @@ def print_mutation(hyp, results):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--epochs', type=int, default=273, help='number of epochs')
-    parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
+    parser.add_argument('--batch-size', type=int, default=4, 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_10img.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='pixels')
+    parser.add_argument('--img-size', type=int, default=320, help='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=2, help='number of Pytorch DataLoader workers')