diff --git a/train.py b/train.py
index e5af1d00..f2fde133 100644
--- a/train.py
+++ b/train.py
@@ -20,10 +20,6 @@ last = wdir + 'last.pt'
 best = wdir + 'best.pt'
 results_file = 'results.txt'
 
-# last = '../coco/last_sgd273_default_ltrb25x4.pt'
-# best = '../coco/best_sgd273_default_ltrb25x4.pt'
-# results_file = '../coco/results_sgd273_default_ltrb25x4.txt'
-
 # Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310
 hyp = {'giou': 1.582,  # giou loss gain
        'cls': 27.76,  # cls loss gain  (CE=~1.0, uCE=~20)
@@ -390,8 +386,8 @@ if __name__ == '__main__':
     parser.add_argument('--epochs', type=int, default=273)  # 500200 batches at bs 16, 117263 images = 273 epochs
     parser.add_argument('--batch-size', type=int, default=32)  # effective bs = batch_size * accumulate = 16 * 4 = 64
     parser.add_argument('--accumulate', type=int, default=2, help='batches to accumulate before optimizing')
-    parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp-2overlap.cfg', help='cfg file path')
-    parser.add_argument('--data', type=str, default='data/coco_64img.data', help='*.data file path')
+    parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
+    parser.add_argument('--data', type=str, default='data/coco.data', help='*.data file path')
     parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches')
     parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
     parser.add_argument('--rect', action='store_true', help='rectangular training')