Restart mean calculation of blobFromImage

Question

I'm training a Yolo model by using cv2.dnn and blobFromImage. I have a df with all the images paths, which i iterate over, to obtain the features, through blobFromImage. So far, I have this:

for i in df.iloc:
    img = cv2.imread(str(i[8]))
    height, width, shape = img.shape
    blob = cv2.dnn.blobFromImage(img, 1/255, (416,416), (0,0,0), True, crop = False) # extract features. Normalize and resize. Swap RGB colours
    print(blob.shape)
    net = cv2.dnn.readNet(path_cfg, path_weights)
    layer_names = net.getLayerNames()
    outputlayers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    net.setInput(blob)
    outs = net.forward(outputlayers)

All my images are of shape (1024, 1024, 3). When I pass the df into the code, blob.shape is (1,3,416,416) in the majority of cases. However, for some images, it reshapes to other size, such as (1,3,814,450). The interesting thing is that if I create a df1 with that specific image path and pass it into the loop, the shape of the blob turns out correctly to (1,3,416,416). Therefore, I'm assuming that it takes some values from the previously passed images.

I would highly appreciate any help which would explain why this is happening and how to solve, so that all blobs are of shape (1,3,416,416).

Many thanks in advance

I expect all blobs to have (1,3,416,416) shape. Some turn out to be different, although all original images are of the same shape.