diff --git a/train.py b/train.py
index f2fde133..e5af1d00 100644
--- a/train.py
+++ b/train.py
@@ -20,6 +20,10 @@ 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)
@@ -386,8 +390,8 @@ 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.cfg', help='cfg file path')
-    parser.add_argument('--data', type=str, default='data/coco.data', help='*.data file path')
+    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('--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')
diff --git a/utils/datasets.py b/utils/datasets.py
index ae5d89ce..6f891365 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -644,7 +644,7 @@ def cutout(image, labels):
         return inter_area / box2_area
 
     # create random masks
-    scales = [0.5] * 1  # + [0.25] * 4 + [0.125] * 16  # image size fraction
+    scales = [0.5] * 1  # + [0.25] * 4 + [0.125] * 16 + [0.0625] * 64 + [0.03125] * 256  # image size fraction
     for s in scales:
         mask_h = random.randint(1, int(h * s))
         mask_w = random.randint(1, int(w * s))
@@ -660,7 +660,7 @@ def cutout(image, labels):
         image[ymin:ymax, xmin:xmax] = mask_color
 
         # return unobscured labels
-        if len(labels):
+        if len(labels) and s > 0.03:
             box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
             ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
             labels = labels[ioa < 0.90]  # remove >90% obscured labels