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Add RetinaNet Object detection with Backbones #529
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Codecov Report
@@ Coverage Diff @@
## master #529 +/- ##
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Coverage 73% 73%
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Files 125 131 +6
Lines 7884 8552 +668
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+ Hits 5737 6259 +522
- Misses 2147 2293 +146 |
This PR also bumps minimum torchvision to 0.8.1 and PyTorch to 1.7. Also this version bump enables us to use CUDA 11 compatible GPUs Ampere Architecture (A100, RTX30XX series). |
…bolts into add_retina
…g-bolts into add_retina
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I left some comments to ease further discussions :)
The RetinaNet test is very slow, the reason being not using Batched NMS implementation. This is fixed in torchvision master and we can expect faster RetinaNet model + tests in next torchvision release |
Co-authored-by: Akihiro Nitta <nitta@akihironitta.com>
@Borda @akihironitta can you have look 😅 |
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It'd be nice to hear your thoughts!
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I believe this PR is ready for getting merged.
Just for the note, here's the summary of my last tens of commits:
- Updated CHANGELOG
- Added its doc
- Reverted the change in Faster R-CNN as it's irrelevant to this PR
- Made RetinaNet use
torchvision.ops.box_iou
instead of our in-housepl_bolts.metrics.iou
- Resolved all the failing tests related to this PR
cc @Borda
so if we set py3.7 as the min version then we do not need to upgrade PL? UPDATE: also if we replace the |
TODO:
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for more information, see https://pre-commit.ci
Looks great. So kind of you @akihironitta Aki : 💖 |
Thanks a lot to @akihironitta and @Borda for your patience. I'm Extremely sorry for this being delayed. |
@oke-aditya Hey Aditya, not at all! Thank you for your work and patience for almost a year 🎉 |
What does this PR do?
Fixes #391
Before submitting
PR review
Did you have fun?
I think yes 😛