ood_samples parameter and # of samples in Table 11

[alinlab]

ood_samples parameter and # of samples in Table 11

[jihoontack]

Thank you for the interest!

I believe your question can be summarized as follows:
(1) Is the ood_sample parameter in eval.py and # of samples in Table 11 the same?
(2) The ood_sample parameter doesn't show dramatic performance as Table 11.
(3) How to achieve controlled results (of Table 11) in our code.
by the way, you don't need to retrain the network to achieve the same results.

We will answer in following orders: (1) -> (3) -> (2)
(1) Yes, the ood_sample parameter in eval.py denotes the # of samples in Table 11.
(3) Our evaluation examples in README.md is a controlled version. If you want an uncontrolled version, first remove --resize-fix and set --resize_factor to 0.08.

• --resize_factor decides the resizing(i.e., cropping) area range (in Inception crop, we uniformly sample from 0.08~1.0 to obtain the size for cropped image).
• --resize_fix fixed the sampling range. For instance, if you set --resize_factor to 0.54 and set --resize_fix, the size of the cropped image will have 0.54 * original image size.

(2) I think that you have run a controlled version with different # of samples. Is it possible to rerun the inference with the uncontrolled version as mentioned in the answer (3) above (to obtain the same result with Table 11)?

If you have more questions please let us know!

Thank you

[jihoontack]

If you have more questions or any problems with running the code, feel free to reopen this issue.

Thank you!