diff --git a/train.py b/train.py
index 0f939769..2b93431e 100644
--- a/train.py
+++ b/train.py
@@ -138,7 +138,7 @@ def train(
     # plt.savefig('LR.png', dpi=300)
 
     # Dataset
-    dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, rect=False, image_weights=False)
+    dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, rect=False, cache=True)
 
     # Initialize distributed training
     if torch.cuda.device_count() > 1:
diff --git a/utils/datasets.py b/utils/datasets.py
index 5ebbc4de..c4395ccd 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -130,7 +130,7 @@ class LoadWebcam:  # for inference
 
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
-    def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False):
+    def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False, cache=False):
         with open(path, 'r') as f:
             img_files = f.read().splitlines()
             self.img_files = list(filter(lambda x: len(x) > 0, img_files))
@@ -185,7 +185,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             self.batch = bi  # batch index of image
 
         # Preload images
-        if n < 1001:  # preload all images into memory if possible
+        if cache & n < 1001:  # preload all images into memory if possible
             self.imgs = [cv2.imread(self.img_files[i]) for i in tqdm(range(n), desc='Reading images')]
 
         # Preload labels (required for weighted CE training)
diff --git a/utils/gcp.sh b/utils/gcp.sh
index ddb6eeed..7bd6e123 100755
--- a/utils/gcp.sh
+++ b/utils/gcp.sh
@@ -71,6 +71,7 @@ gsutil cp -r gs://sm4/supermarket2 .  # dataset from bucket
 rm -rf darknet && git clone https://github.com/AlexeyAB/darknet && cd darknet && wget -c https://pjreddie.com/media/files/darknet53.conv.74  # sudo apt install libopencv-dev && make
 ./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2-1cls.cfg darknet53.conv.74 -map -dont_show # train
 ./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2-1cls.cfg backup/yolov3-spp-sm2-1cls_last.weights  # resume
+python3 train.py --data ../supermarket2/supermarket2.data --cfg cfg/yolov3-spp-sm2-1cls.cfg  # test
 python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/backup/yolov3-spp-sm2-1cls_3000.weights  # test
 gsutil cp -r backup/*.weights gs://sm4/weights  # weights to bucket