avickars / istr Goto Github PK

This is the project page for the paper:

ISTR: End-to-End Instance Segmentation via Transformers.

• (2022.03.09) New codes for ISTR-PCA, ISTR-DCT, and ISTR-SMT with better performance and speed have been released.
• (2021.05.03) The project page for ISTR is avaliable.

• The inference time is evaluated with a single 2080Ti GPU.
• We use the models pre-trained on ImageNet using torchvision. The ImageNet pre-trained ResNet-101 backbone is obtained from SparseR-CNN.

The codes are built on top of Detectron2, SparseR-CNN, and AdelaiDet.

• Python=3.8
• PyTorch=1.6.0, torchvision=0.7.0, cudatoolkit=10.1
• OpenCV for visualization

1. Install the repository (we recommend to use Anaconda for installation.)

conda create -n ISTR python=3.8 -y
conda activate ISTR
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
or (conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch)
pip install opencv-python
pip install scipy
pip install shapely
git clone https://github.com/hujiecpp/ISTR.git
cd ISTR
python setup.py build develop

2. Link coco dataset path

ln -s /coco_dataset_path/coco ./datasets

3. Train ISTR (e.g., with ResNet50 backbone)

python projects/ISTR/train_net.py --num-gpus 4 --config-file projects/ISTR/configs/ISTR-R50-3x.yaml

4. Evaluate ISTR (e.g., with ResNet50 backbone)

python projects/ISTR/train_net.py --num-gpus 4 --config-file projects/ISTR/configs/ISTR-R50-3x.yaml --eval-only MODEL.WEIGHTS ./output/model_final.pth

5. Visualize the detection and segmentation results (e.g., with ResNet50 backbone)

python demo/demo.py --config-file projects/ISTR/configs/ISTR-R50-3x.yaml --input input1.jpg --output ./output --confidence-threshold 0.4 --opts MODEL.WEIGHTS ./output/model_final.pth

If our paper helps your research, please cite it in your publications:

@article{hu2021istr,
  title={Istr: End-to-end instance segmentation with transformers},
  author={Hu, Jie and Cao, Liujuan and Lu, Yao and Zhang, ShengChuan and Wang, Yan and Li, Ke and Huang, Feiyue and Shao, Ling and Ji, Rongrong},
  journal={arXiv preprint arXiv:2105.00637},
  year={2021}
}