README

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.

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.

<!-- ./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 --> <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Name</th> <th valign="bottom">lr sched</th> <th valign="bottom">train time (s/iter)</th> <th valign="bottom">inference time (s/im)</th> <th valign="bottom">train mem (GB)</th> <th valign="bottom">box AP</th> <th valign="bottom">mask AP</th> <th valign="bottom">kp. AP</th> <th valign="bottom">model id</th> <th valign="bottom">download</th> <!-- TABLE BODY --> <!-- ROW: faster_rcnn_R_50_FPN_noaug_1x --> <tr><td align="left"><a href="faster_rcnn_R_50_FPN_noaug_1x.yaml">Faster R-CNN</a></td> <td align="center">1x</td> <td align="center">0.219</td> <td align="center">0.038</td> <td align="center">3.1</td> <td align="center">36.9</td> <td align="center"></td> <td align="center"></td> <td align="center">137781054</td> <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>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/metrics.json">metrics</a></td> </tr> <!-- ROW: keypoint_rcnn_R_50_FPN_1x --> <tr><td align="left"><a href="keypoint_rcnn_R_50_FPN_1x.yaml">Keypoint R-CNN</a></td> <td align="center">1x</td> <td align="center">0.313</td> <td align="center">0.071</td> <td align="center">5.0</td> <td align="center">53.1</td> <td align="center"></td> <td align="center">64.2</td> <td align="center">137781195</td> <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>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/metrics.json">metrics</a></td> </tr> <!-- ROW: mask_rcnn_R_50_FPN_noaug_1x --> <tr><td align="left"><a href="mask_rcnn_R_50_FPN_noaug_1x.yaml">Mask R-CNN</a></td> <td align="center">1x</td> <td align="center">0.273</td> <td align="center">0.043</td> <td align="center">3.4</td> <td align="center">37.8</td> <td align="center">34.9</td> <td align="center"></td> <td align="center">137781281</td> <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>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/metrics.json">metrics</a></td> </tr> </tbody></table>

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 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 for details.

For speed comparison, see benchmarks.