liuguoyou / sparser-cnn

End-to-End Object Detection with Learnable Proposal

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

Paper

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

Models

Method	inf_time	train_time	box AP	download
R50_100pro_3x	23 FPS	19h	42.3	model is coming.

Notes

• More settings are coming.
• We observe about 0.3 AP noise.
• The training time is on 8 GPUs with batchsize 16. The inference time is on single GPU. All GPUs are NVIDIA V100.

Installation

The codebases are built on top of Detectron2 and DETR.

Requirements

• Linux or macOS with Python ≥ 3.6
• PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this
• OpenCV is optional and needed by demo and visualization

Steps

1. Install and build libs

git clone https://github.com/PeizeSun/SparseR-CNN.git
cd SparseR-CNN
python setup.py build develop

2. Link coco dataset path to SparseR-CNN/datasets/coco

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

3. Train SparseR-CNN

python projects/SparseR-CNN/train_net.py --num-gpus 8 \
    --config-file projects/SparseR-CNN/configs/sparsercnn.res50.100pro.3x.yaml

4. Evaluate SparseR-CNN

python projects/SparseR-CNN/train_net.py --num-gpus 8 \
    --config-file projects/SparseR-CNN/configs/sparsercnn.res50.100pro.3x.yaml \
    --eval-only MODEL.WEIGHTS path/to/model.pth

License

SparseR-CNN is released under MIT License.

Citing

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, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, Ping Luo},
  journal =  {arXiv preprint arXiv:},
  year    =  {2020}
}