More robust Accuracy module #3155
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Johnson-Wang
commented
Jun 30, 2020
Johnson-Wang
commented
- Make mmdet.models.Accuracy class more robust to empty predictions.
- Add score threshold for Accuracy calculation
- Add unittest for Accuracy module
- change accuracy to pos_recall in FSAF and use Accuracy module to calculate it.
…to pos_recall # Conflicts: # mmdet/models/dense_heads/fsaf_head.py
yhcao6
reviewed
Jul 2, 2020
mmdet/models/losses/accuracy.py
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| topk (int | tuple[int], optional): If the predictions in ``topk`` | ||
| matches the target, the predictions will be regarded as | ||
| correct ones. Defaults to 1. | ||
| thresh (float, optional): If not None, threshold for pred with scores | ||
| under this value being incorrect. Default to None. |
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This sentence is a little confusing. Is it better to say this?
If not None, predictions with scores under this threshold are to be considered incorrect
yhcao6
reviewed
Jul 2, 2020
mmdet/models/losses/accuracy.py
Outdated
| """Module to calculate the accuracy | ||
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| Args: | ||
| topk (tuple, optional): The criterion used to calculate the | ||
| accuracy. Defaults to (1,). | ||
| thresh (float, optional): If not None, threshold for prediction | ||
| scores under this value being incorrect. Default to None. |
yhcao6
approved these changes
Jul 3, 2020
mike112223
pushed a commit
to mike112223/mmdetection
that referenced
this pull request
Aug 25, 2020
* change accuracy to positive recall in FSAF * use mmdet accuracy module to calculate pos recall in fsaf * Change label to long in unittest * revise docstring according to comments * revise docstring according to comments
FANGAreNotGnu
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Oct 23, 2023
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