85 lines
4.2 KiB
Markdown
85 lines
4.2 KiB
Markdown
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Detectron2 model zoo's experimental settings and a few implementation details are different from Detectron.
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The differences in implementation details are shared in
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[Compatibility with Other Libraries](../../docs/notes/compatibility.md).
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The differences in model zoo's experimental settings include:
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* Use scale augmentation during training. This improves AP with lower training cost.
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* Use L1 loss instead of smooth L1 loss for simplicity. This sometimes improves box AP but may
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affect other AP.
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* Use `POOLER_SAMPLING_RATIO=0` instead of 2. This does not significantly affect AP.
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* Use `ROIAlignV2`. This does not significantly affect AP.
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In this directory, we provide a few configs that __do not__ have the above changes.
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They mimic Detectron's behavior as close as possible,
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and provide a fair comparison of accuracy and speed against Detectron.
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<!--
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./gen_html_table.py --config 'Detectron1-Comparisons/*.yaml' --name "Faster R-CNN" "Keypoint R-CNN" "Mask R-CNN" --fields lr_sched train_speed inference_speed mem box_AP mask_AP keypoint_AP --base-dir ../../../configs/Detectron1-Comparisons
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-->
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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<th valign="bottom">Name</th>
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<th valign="bottom">lr<br/>sched</th>
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<th valign="bottom">train<br/>time<br/>(s/iter)</th>
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<th valign="bottom">inference<br/>time<br/>(s/im)</th>
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<th valign="bottom">train<br/>mem<br/>(GB)</th>
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<th valign="bottom">box<br/>AP</th>
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<th valign="bottom">mask<br/>AP</th>
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<th valign="bottom">kp.<br/>AP</th>
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<th valign="bottom">model id</th>
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<th valign="bottom">download</th>
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<!-- TABLE BODY -->
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<!-- ROW: faster_rcnn_R_50_FPN_noaug_1x -->
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<tr><td align="left"><a href="faster_rcnn_R_50_FPN_noaug_1x.yaml">Faster R-CNN</a></td>
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<td align="center">1x</td>
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<td align="center">0.219</td>
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<td align="center">0.038</td>
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<td align="center">3.1</td>
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<td align="center">36.9</td>
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<td align="center"></td>
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<td align="center"></td>
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<td align="center">137781054</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/model_final_7ab50c.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
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<tr><td align="left"><a href="keypoint_rcnn_R_50_FPN_1x.yaml">Keypoint R-CNN</a></td>
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<td align="center">1x</td>
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<td align="center">0.313</td>
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<td align="center">0.071</td>
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<td align="center">5.0</td>
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<td align="center">53.1</td>
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<td align="center"></td>
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<td align="center">64.2</td>
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<td align="center">137781195</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/model_final_cce136.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/metrics.json">metrics</a></td>
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</tr>
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<!-- ROW: mask_rcnn_R_50_FPN_noaug_1x -->
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<tr><td align="left"><a href="mask_rcnn_R_50_FPN_noaug_1x.yaml">Mask R-CNN</a></td>
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<td align="center">1x</td>
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<td align="center">0.273</td>
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<td align="center">0.043</td>
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<td align="center">3.4</td>
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<td align="center">37.8</td>
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<td align="center">34.9</td>
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<td align="center"></td>
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<td align="center">137781281</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/model_final_62ca52.pkl">model</a> | <a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/metrics.json">metrics</a></td>
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</tr>
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</tbody></table>
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## Comparisons:
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* Faster R-CNN: Detectron's AP is 36.7, similar to ours.
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* Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's
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[bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be
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compensated back by some parameter tuning.
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* 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.
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See [this article](https://ppwwyyxx.com/blog/2021/Where-are-Pixels/) for details.
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For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html).
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