Comments (3)
Sorry for the very late response!
Looks like I introduced some issues in a more recent change I made. I'll get these fixed soon.
In general, for reproducibility, the best source might be https://worksheets.codalab.org/worksheets/0xb6d027ee127e422989ab9115726c5411
This Codalab worksheet contains the exact code to reproduce experiments in the paper. For example, for the experiment you're looking at I believe you want the bundle: https://worksheets.codalab.org/bundles/0xd9d037f3de8a4b31be4072f3b75735b1 and under "dependencies" you can see the exact code that produced the run. You can also download the code and Docker containers.
In any case, will get this fixed asap!
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This should be fixed! Let me know if you have any issues. For ImageNet logits, please download them at: https://worksheets.codalab.org/bundles/0x81c9c8a9bf6c47f59f45f6fc80790c3c and put them into the data folder (otherwise the ImageNet experiment won't run)
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Closing for now, but feel free to reopen if you have follow-up questions or it doesn't work!
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Related Issues (16)
- Question about num_calibration HOT 2
- Error if a bin has only a single class in it
- Incorrect scikit-learn package requirement
- Bootstrap uncertainty details HOT 1
- Incorrect type hints
- Unused variable
- Undefined variable
- Function parameter not used
- The number orders are not consistent before/after calibration HOT 2
- When a bin has samples of the same probability
- Unable to obtain calibration error when missing a class HOT 2
- Question: Reliability diagrams HOT 3
- Pickle trained Calibrators HOT 4
- Calibrated probabilites from "top" calibrators HOT 1
- Calculate calibration error with softmax distribution and missing class
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