Awesome
Deeply Shape-guided Cascade for Instance Segmentation
This repo hosts the code for implementing the DSC algorithms for instance segmentation.
Deeply Shape-guided Cascade for Instance Segmentation,
Hao Ding, Siyuan Qiao, Alan Yuille, Wei Shen
In: Proc. IEEE Conference Computer Vision Pattern Recognition(CVPR), 2021
arXiv preprint (arXiv 1911.11263)
Highlights
- Imposing shape guidance: DSC improved the original HTC baseline by imposing shape guidance both explicitly and implicitly.
- State-of-the-art performance: Our best single model based on ResNeXt-101-64x4d + FPN with deformable convolutions achieves 51.8% in box AP and 45.5% in mask AP on COCO test-dev (without multi-scale testing)
- Better improvement for huddled instances: With shape guidance, DSC's improvement is more significant on images with more huddled instances.
Installation
This implementation is based on mmdetection(v2.1.0). Please refer to INSTALL.md GET_STARTED.md for installation and dataset preparation. A list of installed packages information from anaconda is provided in conda_list.txt
Model Zoo
We provide the following trained models on COCO
| Model | Backbone | Multi-scale training | Lr schd | box AP | Mask AP | config | Model |
|---|---|---|---|---|---|---|---|
| F-DSC | R50-FPN | No | 1x | 44.5 | 39.5 | config | download |
| DSC | R50-FPN | No | 1x | 45.0 | 39.7 | config | download |
| DSC | R50-FPN | No | 20e | 45.8 | 40.1 | config | download |
| DSC | R101-FPN | No | 20e | 46.6 | 40.7 | config | download |
| DSC | X101-32x4d-FPN | No | 20e | 48.0 | 41.9 | config | download |
| DSC | X101-64x4d-DCN-FPN | Yes | 20e | 51.4 | 44.9 | config | download |
Usage
Train with multiple GPUs
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM}
Example:
./tools/dist_train.sh configs/dsc/fast_dsc_r50_fpn_1x_coco.py 8
Train with single GPU
python tools/train.py ${CONFIG_FILE}
Example:
python tools/train.py configs/dsc/fast_dsc_r50_fpn_1x_coco.py
Testing
# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} --out ${OUTPUT_FILE} --eval bbox segm
Example:
./tools/dist_test.sh configs/dsc/fast_dsc_r50_fpn_1x_coco.py f_dsc_r50_fpn_1x_coco.pth 8 --out results.pkl --eval bbox segm
# single-gpu testing
python tools/test_ins.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --out ${OUTPUT_FILE} --eval segm
Example:
python tools/test_ins.py configs/dsc/fast_dsc_r50_fpn_1x_coco.py f_dsc_r50_fpn_1x_coco.pth --out results.pkl --eval bbox segm
Citations
Please consider citing our papers in your publications if this repo helps you.
@inproceedings{ding2021dsc,
title = {Deeply Shape-guided Cascade for Instance Segmentation},
author = {Ding, Hao and Qiao, Siyuan and Yuille, Alan and Shen, Wei},
booktitle = {IEEE Conf. Comput. Vis. Pattern Recog. (CVPR)},
year = {2021}
}
License
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Hao Ding (email) and Wei Shen.