Official implementation of Hier R-CNN: Instance-level Human Parts Detection and A New Benchmark (under review)
In this repository, we release the COCO Human Parts dataset and Hier R-CNN code in Pytorch.
- Hier R-CNN architecture:
- Hier R-CNN output:
- 8 x TITAN Xp GPU
- pytorch1.1
- python3.6.8
Install Hier R-CNN following INSTALL.md.
You can download the annotations of COCO Human Parts here. And following Data to train or evaluate Hier R-CNN models.
| Backbone | LR | MS training | DCN | Det AP | Sub AP | DOWNLOAD |
|---|---|---|---|---|---|---|
| R-50-FPN | 1x | No | No | 36.8 | 20.0 | GoogleDrive |
| R-50-FPN | 2x | Yes | No | 39.3 | 23.2 | |
| R-50-FPN | 1x | No | Yes | 38.6 | 21.9 | |
| R-50-FPN | 2x | Yes | Yes | 40.6 | 23.0 | GoogleDrive |
| R-101-FPN | 1x | No | No | 37.2 | 20.6 | |
| X-101-FPN | 1x | No | No | 38.8 | 22.2 | |
| X-101-FPN | 2x | Yes | No | 40.5 | 24.1 | |
| X-101-FPN | 1x | No | Yes | 40.3 | 22.9 | |
| X-101-FPN | 2x | Yes | Yes | 42.0 | 24.2 | GoogleDrive |
ImageNet pretrained weights
To train a model with 8 GPUs run:
python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --cfg cfgs/mscoco_humanparts/e2e_hier_rcnn_R-50-FPN_1x.yaml
python tools/test_net.py --cfg ckpts/mscoco_humanparts/e2e_hier_rcnn_R-50-FPN_1x/e2e_hier_rcnn_R-50-FPN_1x.yaml --gpu_id 0,1,2,3,4,5,6,7
python tools/test_net.py --cfg ckpts/mscoco_humanparts/e2e_hier_rcnn_R-50-FPN_1x/e2e_hier_rcnn_R-50-FPN_1x.yaml --gpu_id 0
Hier-R-CNN is released under the MIT license.
