diff --git a/train.py b/train.py
index 4adb68eb..67038e85 100644
--- a/train.py
+++ b/train.py
@@ -143,15 +143,15 @@ def train(
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
 
     # Start training
-    t, t0 = time.time(), time.time()
     model.hyp = hyp  # attach hyperparameters to model
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
     model_info(model)
     nb = len(dataloader)
     results = (0, 0, 0, 0, 0)  # P, R, mAP, F1, test_loss
     n_burnin = min(round(nb / 5 + 1), 1000)  # burn-in batches
-    os.remove('train_batch0.jpg') if os.path.exists('train_batch0.jpg') else None
-    os.remove('test_batch0.jpg') if os.path.exists('test_batch0.jpg') else None
+    for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg'):
+        os.remove(f)
+    t, t0 = time.time(), time.time()
     for epoch in range(start_epoch, epochs):
         model.train()
         print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
@@ -282,7 +282,7 @@ if __name__ == '__main__':
     parser.add_argument('--img-size', type=int, default=416, 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=2, help='number of Pytorch DataLoader workers')
+    parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
     parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
     parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
     parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
diff --git a/utils/datasets.py b/utils/datasets.py
index 147dc077..22b6c4ba 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -318,7 +318,7 @@ def letterbox(img, new_shape=416, color=(127.5, 127.5, 127.5), mode='auto'):
 
     top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
     left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_AREA)  # resized, no border
+    img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)  # resized, no border
     img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # padded square
     return img, ratio, dw, dh