No validation of the listed accuracy values in the README possible

[udrechsler]

Hi, it is not clear (at least to me) how the numbers in the README are generated. I'm trying to replicate/validate the listed accuracy values for the last few days, but I'm always off by at least a few percent, regardless what pre-processing I'm using. Would it be possible to add the script used to generate these numbers?

Best regards, Drechsler

[taehoonlee]

@udrechsler,

I will share my inference codes in the near future. Key recipes for ImageNet are the following:

• Down-sampling should not be performed as a square. Adjust shorten side as 256 is the right way. In other words, a resized image is either one of (256 X W) or (H X 256).
• The down-sampling scheme is pretty important. I evaluated all the models with cv2.resize(img, (h, w), interpolation=cv2.INTER_CUBIC), while the keras default is img.resize((w, h), pil_image.NEAREST). You can try pil_image.BICUBIC or opencv2 like me.
• After the down-sampling, you should crop the 224x224 center region from an (256 X W) or (H X 256) image.

[qwerybot]

I'm struggling to get the same numbers shown in the README as well, I consistently fall 7-8% behind what is shown. I've tried implementing the method @taehoonlee described, but I don't think I fully understand the process as my accuracy dropped further. Have you released the code yet?

[qwerybot]

I think I've got it working now. What do you do in the cases where an image is smaller than 224x224 to begin with?

[taehoonlee]

@BenTaylor3115, Please just keep the ratio 7/8(=224/256). And as far as I know, there are no examples where the image sizes are smaller than 224 on the official ImageNet results.

[qwerybot]

I didn't think so. I may still have a problem somewhere. Do you have the code available for the down-sampling / cropping you used to achieve the results in the README? I'm happy to do the debugging myself if I have a reference of the correct way.