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Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Method | inf_time | train_time | box AP | download |
---|---|---|---|---|
R50_100pro_3x | 23 FPS | 19h | 42.8 | model | log |
R50_300pro_3x | 22 FPS | 24h | 45.0 | model | log |
R101_100pro_3x | 19 FPS | 25h | 44.1 | model | log |
R101_300pro_3x | 18 FPS | 29h | 46.4 | model | log |
Models and logs are available in Baidu Drive by code wt9n.
Method | inf_time | train_time | box AP | codebase |
---|---|---|---|---|
R50_300pro_3x | 22 FPS | 24h | 45.0 | detectron2 |
R50_300pro_3x.detco | 22 FPS | 28h | 46.5 | detectron2 |
PVTSmall_300pro_3x | 13 FPS | 50h | 45.7 | mmdetection |
PVTv2-b2_300pro_3x | 11 FPS | 76h | 50.1 | mmdetection |
The codebases are built on top of Detectron2 and DETR.
git clone https://github.com/PeizeSun/SparseR-CNN.git
cd SparseR-CNN
python setup.py build develop
mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017
python projects/SparseRCNN/train_net.py --num-gpus 8 \
--config-file projects/SparseRCNN/configs/sparsercnn.res50.100pro.3x.yaml
python projects/SparseRCNN/train_net.py --num-gpus 8 \
--config-file projects/SparseRCNN/configs/sparsercnn.res50.100pro.3x.yaml \
--eval-only MODEL.WEIGHTS path/to/model.pth
python demo/demo.py\
--config-file projects/SparseRCNN/configs/sparsercnn.res50.100pro.3x.yaml \
--input path/to/images --output path/to/save_images --confidence-threshold 0.4 \
--opts MODEL.WEIGHTS path/to/model.pth
SparseR-CNN is released under MIT License.
If you use SparseR-CNN in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:
@article{peize2020sparse,
title = {{SparseR-CNN}: End-to-End Object Detection with Learnable Proposals},
author = {Peize Sun and Rufeng Zhang and Yi Jiang and Tao Kong and Chenfeng Xu and Wei Zhan and Masayoshi Tomizuka and Lei Li and Zehuan Yuan and Changhu Wang and Ping Luo},
journal = {arXiv preprint arXiv:2011.12450},
year = {2020}
}
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