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vton-api/preprocess/humanparsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/README.md Normal file
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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.
<!--
./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<br/>sched</th>
<th valign="bottom">train<br/>time<br/>(s/iter)</th>
<th valign="bottom">inference<br/>time<br/>(s/im)</th>
<th valign="bottom">train<br/>mem<br/>(GB)</th>
<th valign="bottom">box<br/>AP</th>
<th valign="bottom">mask<br/>AP</th>
<th valign="bottom">kp.<br/>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](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.
For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html).

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vton-api/preprocess/humanparsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml Normal file
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
# Detectron1 uses smooth L1 loss with some magic beta values.
# The defaults are changed to L1 loss in Detectron2.
RPN:
SMOOTH_L1_BETA: 0.1111
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
INPUT:
# no scale augmentation
MIN_SIZE_TRAIN: (800, )

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vton-api/preprocess/humanparsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml Normal file
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
KEYPOINT_ON: True
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 1
ROI_KEYPOINT_HEAD:
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
# Detectron1 uses smooth L1 loss with some magic beta values.
# The defaults are changed to L1 loss in Detectron2.
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
RPN:
SMOOTH_L1_BETA: 0.1111
# Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2
# 1000 proposals per-image is found to hurt box AP.
# Therefore we increase it to 1500 per-image.
POST_NMS_TOPK_TRAIN: 1500
DATASETS:
TRAIN: ("keypoints_coco_2017_train",)
TEST: ("keypoints_coco_2017_val",)

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vton-api/preprocess/humanparsing/mhp_extension/detectron2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml Normal file
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_BASE_: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: True
RESNETS:
DEPTH: 50
# Detectron1 uses smooth L1 loss with some magic beta values.
# The defaults are changed to L1 loss in Detectron2.
RPN:
SMOOTH_L1_BETA: 0.1111
ROI_BOX_HEAD:
SMOOTH_L1_BETA: 1.0
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
ROI_MASK_HEAD:
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
INPUT:
# no scale augmentation
MIN_SIZE_TRAIN: (800, )