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PatchDCT: Patch Refinement for High Quality Instance Segmentation(ICLR 2023)

[PatchDCT: Patch Refinement for High Quality Instance Segmentation] Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang

arXiv preprint(arXiv:2302.02693)

In this repository, we release code for PatchDCT in Detectron2.

Contributions

PatchDCT is the fist compressed vector based multi-stage refinement framework.
By using a classifier to refine foreground and background patches, and predicting an informative low-dimensional DCT vector for each mixed patch, PatchDCT generates high-resolution masks with fine boundaries and low computational cost.

Installation

Requirements

PyTorch ≥ 1.8

This implementation is based on detectron2. Please refer to INSTALL.md. for installation and dataset preparation.

Usage

The codes of this project is on projects/PatchDCT/

Train with multiple GPUs

cd ./projects/PatchDCT/
./train.sh

Testing

cd ./projects/PatchDCT/
./test.sh

Speed Testing

cd ./projects/PatchDCT/
./test_speed.sh

Upper Bound of Model Performance(Table 1 in the paper)

cd ./projects/PatchDCT/
./test_up.sh

For Swin-B backbone, use train_net_swinb.py instead of train_net.py

Model ZOO

Trained models on COCO

Model	Backbone	Schedule	Multi-scale training	FPS	AP (val)	Link
PatchDCT	R50	1x	Yes	12.3	37.2	download
PatchDCT	R101	3x	Yes	11.8	40.5	download
PatchDCT	RX101	3x	Yes	11.7	41.8	download
PatchDCT	SwinB	3x	Yes	7.3	46.1	download

Trained models on Cityscapes

Model	Data	Backbone	Schedule	Multi-scale training	AP (val)	Link
PatchDCT	Fine-Only	R50	1x	Yes	38.2	download
PatchDCT	COCO Pretrain+Fine	R50	1x	Yes	40.3	download

Notes

We observe about 0.2 AP noise in COCO.
The inference time is measured on NVIDIA A100 with batchsize=1.
Lvis 0.5 is used for evaluation.

Contributing to the project

Any pull requests or issues are welcome.

If there is any problem with this project, please contact Qinrou Wen.

Citations

Please consider citing our papers in your publications if the project helps your research.

License

MIT License.