DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.
Shiyi Lan, Zhiding Yu, Chris Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry Davis, Anima Anandkumar
International Conference on Computer Vision (ICCV) 2021
This repository contains the official Pytorch implementation of training & evaluation code and pretrained models for DiscoBox. DiscoBox is a state of the art framework that can jointly predict high quality instance segmentation and semantic correspondence from box annotations.
We use MMDetection v2.10.0 as the codebase.
All of our models are trained and tested using automatic mixed precision, which leverages float16 for speedup and less GPU memory consumption.
This implementation is based on PyTorch==1.9.0, mmcv==2.13.0, and mmdetection==2.10.0
Please refer to get_started.md for installation.
Or you can download the docker image from our dockerhub repository.
| Backbone | Weights | AP | AP@50 | AP@75 | AP@Small | AP@Medium | AP@Large |
|---|---|---|---|---|---|---|---|
| ResNet-50 | download | 30.7 | 52.6 | 30.6 | 13.3 | 34.1 | 45.6 |
| ResNet-101-DCN | download | 35.3 | 59.1 | 35.4 | 16.9 | 39.2 | 53.0 |
| ResNeXt-101-DCN | download | 37.3 | 60.4 | 39.1 | 17.8 | 41.1 | 55.4 |
We also evaluate the models in the section Results on COCO val 2017 with the same weights on COCO test-dev.
| Backbone | Weights | AP | AP@50 | AP@75 | AP@Small | AP@Medium | AP@Large |
|---|---|---|---|---|---|---|---|
| ResNet-50 | download | 32.0 | 53.6 | 32.6 | 11.7 | 33.7 | 48.4 |
| ResNet-101-DCN | download | 35.8 | 59.8 | 36.4 | 16.9 | 38.7 | 52.1 |
| ResNeXt-101-DCN | download | 37.9 | 61.4 | 40.0 | 18.0 | 41.1 | 53.9 |
ResNet-50 (8 GPUs):
bash tools/dist_train.sh \
configs/discobox/discobox_solov2_r50_fpn_3x.py 8
ResNet-101-DCN (8 GPUs):
bash tools/dist_train.sh \
configs/discobox/discobox_solov2_r101_dcn_fpn_3x.py 8
ResNeXt-101-DCN (8 GPUs):
bash tools/dist_train.sh \
configs/discobox/discobox_solov2_x101_dcn_fpn_3x.py 8
ResNet-50 (4 GPUs):
bash tools/dist_train.sh \
configs/discobox/discobox_solov2_voc_r50_fpn_6x.py 4
ResNet-101 (4 GPUs):
bash tools/dist_train.sh \
configs/discobox/discobox_solov2_voc_r101_fpn_6x.py 4
ResNet-50 (8 GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_r50_fpn_3x.py \
work_dirs/coco_r50_fpn_3x.pth 8 --eval segm
ResNet-101-DCN (8 GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_r101_dcn_fpn_3x.py \
work_dirs/coco_r101_dcn_fpn_3x.pth 8 --eval segm
ResNeXt-101-DCN (GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_x101_dcn_fpn_3x_fp16.py \
work_dirs/coco_x101_dcn_fpn_3x.pth 8 --eval segm
ResNet-50 (4 GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_voc_r50_fpn_3x_fp16.py \
work_dirs/voc_r50_6x.pth 4 --eval segm
ResNet-101 (4 GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_voc_r101_fpn_3x_fp16.py \
work_dirs/voc_r101_6x.pth 4 --eval segm
Step 1: generate results
ResNet-50 (4 GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_voc_r50_fpn_3x_fp16.py \
work_dirs/voc_r50_6x.pth 4 \
--format-only \
--options "jsonfile_prefix=work_dirs/voc_r50_results.json"
ResNet-101 (4 GPUs):
bash tools/dist_test.sh \
configs/discobox/discobox_solov2_voc_r101_fpn_3x_fp16.py \
work_dirs/voc_r101_6x.pth 4 \
--format-only \
--options "jsonfile_prefix=work_dirs/voc_r101_results.json"
Step 2: format conversion
ResNet-50:
python tools/json2mat.pywork_dirs/voc_r50_results.json work_dirs/voc_r50_results.mat
ResNet-101:
python tools/json2mat.pywork_dirs/voc_r101_results.json work_dirs/voc_r101_results.mat
Step 3: evaluation
Please visit BBTP for the evaluation code written in Matlab.
Please visit this repository.
Please check the LICENSE file. DiscoBox may be used non-commercially, meaning for research or evaluation purposes only. For business inquiries, please contact [email protected].
@article{lan2021discobox,
title={DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision},
author={Lan, Shiyi and Yu, Zhiding and Choy, Christopher and Radhakrishnan, Subhashree and Liu, Guilin and Zhu, Yuke and Davis, Larry S and Anandkumar, Anima},
journal={arXiv preprint arXiv:2105.06464},
year={2021}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent