Detectron2 model zoo's experimental settings and a few implementation details are different from Detectron. The differences in implementation details are shared in [Compatibility with Other Libraries](../../docs/notes/compatibility.md). The differences in model zoo's experimental settings include: * Use scale augmentation during training. This improves AP with lower training cost. * Use L1 loss instead of smooth L1 loss for simplicity. This sometimes improves box AP but may affect other AP. * Use `POOLER_SAMPLING_RATIO=0` instead of 2. This does not significantly affect AP. * Use `ROIAlignV2`. This does not significantly affect AP. In this directory, we provide a few configs that __do not__ have the above changes. They mimic Detectron's behavior as close as possible, and provide a fair comparison of accuracy and speed against Detectron.

Name	lr sched	train time (s/iter)	inference time (s/im)	train mem (GB)	box AP	mask AP	kp. AP	model id	download
Faster R-CNN	1x	0.219	0.038	3.1	36.9			137781054	model \| metrics
Keypoint R-CNN	1x	0.313	0.071	5.0	53.1		64.2	137781195	model \| metrics
Mask R-CNN	1x	0.273	0.043	3.4	37.8	34.9		137781281	model \| metrics

## Comparisons: * Faster R-CNN: Detectron's AP is 36.7, similar to ours. * Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's [bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be compensated back by some parameter tuning. * Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation. See [this article](https://ppwwyyxx.com/blog/2021/Where-are-Pixels/) for details. For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html).