[GitHub] jonbakerfish opened a new issue #10935: When are the images converted to range(0, 1) in pre-trained resnet50_v2 on imagenet?

[GitBox <gi...@apache.org>]

jonbakerfish opened a new issue #10935: When are the images converted to range(0,1) in pre-trained resnet50_v2 on imagenet?
URL: https://github.com/apache/incubator-mxnet/issues/10935
 
 
   From the Gluon Model Zoo:
   
   > All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]
   
   However, when I look at the train scripts for resnet50_v2 (i.e. https://github.com/apache/incubator-mxnet/blob/master/example/gluon/image_classification.py and https://github.com/apache/incubator-mxnet/blob/master/example/gluon/data.py)
    There is no code line showing that the image is converted to range (0,1) (i.e. `image = image/255`) in the `get_imagenet_iterator`. 
   It seems that the only explanation is that the images are loaded in [0, 1] range by the [ImageFolderDataset](https://github.com/apache/incubator-mxnet/blob/b011eccd17d660b2f1ecf901e0db0007706df35c/python/mxnet/gluon/data/vision/datasets.py#L264). 
   Please correct me if I'm wrong.

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