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AttributeError: module 'scipy.misc' has no attribute 'imread'

imread is depreciated after Scipy version 1.2.0

Hi,

are you aware that the validation set of 49999 images downloaded from here:
http://image-net.org/small/valid_32x32.tar

has a lot of duplicate images? Reproduction of a few examples:
02273.png and 42263.png
04990.png and 45295.png

overall, there are only ~45047 unique images in the validation set - about 5k of them occur twice, and a few even three times. Is that intended to give some examples more weight for validation score, or rather a bug? + wondering if it also applies to 64x64 version - haven't tested that yet

Thanks

Dear PatrykChrabaszcz:
I am using your dataset <ImageNet6464> , I want to re-implement WRN-36-k(1,2) on ImageNet6464 in the Table 1 at page5 . I read your paper and find that at page 3, you describe how to make the WRN-arcitecture. As a result, I draw this two figurs, please help me is it correct??

This is a different that in page 4, the writers said that he changed the order of BN, ReLU, Conv. He changed from conv-BN-ReLU to BN-ReLU-conv. Did you do the same procedure in your paper??

Hi,
What batch size did you set on these experiments? Thanks

Will the author kindly release pretrained weights for the Imagenet64?

So when I download the data from the website, I get a tarfile back. I've tried your unpicle code and that won't work when I provide the tar file path. How exactly should I process this data in order to get to the images and labels? I am not following the scripts as I don't see any code where you process or unzip tarfiles.

Hi there,

I would like to use the downsampled version of ImageNet. I have downloaded the val+train files from https://image-net.org/download-images.php, the "npz format" versions. Then I ran "unzip Imagenet64_val_npz.zip" which resulted in a file called "val_data.npz".

I use python 3.6.8, and trying to load the file with the provided code results in the following error:

def load_data(input_file):

    d = unpickle(input_file)
    x = d['data']
    y = d['labels']

    x = np.dstack((x[:, :4096], x[:, 4096:8192], x[:, 8192:]))
    x = x.reshape((x.shape[0], 64, 64, 3))

    return x, y

def unpickle(file):
    with open(file, 'rb') as fo:
        dict = pickle.load(fo)
    return dict

x, y = load_data("./val64/Imagenet64_val_npz/val_data.npz")
Error: "UnpicklingError: A load persistent id instruction was encountered, but no persistent_load function was specified."

Using x, y = np.load(input_file) works, but the labels seem to be wrong, and the files are also unordered which is fine though. The shapes of x and y are (50000, 64, 64, 3) and (50000,), respectively, so the file downloading, unzipping and loading seems to work (with np.load not with pickle).

These are the first five images in x with the labels y (and the human readable meanings taken from here):

-> Does one have to manipulate the labels y to make them "correct" again?

Thank you for your help :)

Thanks for your great work.
I've found that aspect ratio is changed in this work.
Is there a reason of why this is different to general ImageNet preprocessing? (center crop to keep the aspect ratio)