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Detection: panoptic quality #929
Detection: panoptic quality #929
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for more information, see https://pre-commit.ci
…rger/metrics into feature/50_panoptic_quality
Codecov Report
Additional details and impacted files@@ Coverage Diff @@
## master #929 +/- ##
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- Coverage 88% 37% -51%
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Files 213 216 +3
Lines 10987 11139 +152
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- Hits 9660 4146 -5514
- Misses 1327 6993 +5666 |
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few comments
for more information, see https://pre-commit.ci
…rger/metrics into feature/50_panoptic_quality
Hi @Borda, I just reached out on PTL's Slack to offer help if it's useful to have another pair of hands to bring this PR to the finish line ;) |
for more information, see https://pre-commit.ci
…rger/metrics into feature/50_panoptic_quality
Hey @marcocaccin yes, metrics should always work with batches. |
…rger/metrics into feature/50_panoptic_quality
Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
for more information, see https://pre-commit.ci
@marcocaccin we merged it for now. Feel free to do another PR to add batched-support and also maybe more tests :) |
Hi @niberger, I just wanted to ask if you have verified that your PQ implementation matches the results of the COCO PQ implementation? Thanks :) |
What does this PR do?
Add the panoptic quality metric, as defined in the reference paper.
This implementation differ from the reference implementation of the COCO dataset on the following points:
Tensor
of shape(height, width, 2)
, where the first number of each pixel is the category id and the second number is the instance id.Fixes #50
closes #1435
Before submitting
PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Did you have fun?
Make sure you had fun coding 🙃