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https://github.com/vt-vl-lab/drg
[ECCV 2020] DRG: Dual Relation Graph for Human-Object Interaction Detection
https://github.com/vt-vl-lab/drg
graph-network human-object-interaction
Last synced: about 2 months ago
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[ECCV 2020] DRG: Dual Relation Graph for Human-Object Interaction Detection
- Host: GitHub
- URL: https://github.com/vt-vl-lab/drg
- Owner: vt-vl-lab
- License: mit
- Created: 2020-08-24T04:33:19.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-03-11T16:50:53.000Z (almost 3 years ago)
- Last Synced: 2023-10-26T08:08:32.382Z (about 1 year ago)
- Topics: graph-network, human-object-interaction
- Language: Python
- Homepage:
- Size: 1.33 MB
- Stars: 62
- Watchers: 8
- Forks: 20
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DRG: Dual Relation Graph for Human-Object Interaction Detection
Official Pytorch implementation for [DRG: Dual Relation Graph for Human-Object Interaction Detection (ECCV 2020)](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570681.pdf).
See the [project page](http://chengao.vision/DRG/) for more details. Please contact Jiarui Xu ([email protected]) if you have any questions related to implementation details.
### Prerequisites
This codebase was tested with Python 3.6, Pytorch 1.0 from a nightly release, CUDA 10.0, and CentOS 7.4.1708.
### Installation
Please check [INSTALL.md](INSTALL.md) for installation instructions.
### Data Downloads
Download V-COCO and HICO-DET data. Setup HICO-DET evaluation code.
```Shell
bash ./scripts/download_dataset.sh
bash ./scripts/download_data.sh
```
### Evaluation
1. Download DRG detections and data
```Shell
bash ./scripts/download_drg_detection.sh
```
2. Evaluate on VCOCO
```Shell
python tools/vcoco_compute_mAP.py \
--dataset_name vcoco_test \
--detection_file output/VCOCO/detection_merged_human_object_app.pkl
```
3. Evaluate on HICO-DET
```Shell
cd Data/ho-rcnn
matlab -r "Generate_detection('COCO'); quit"
cd ../../
```
4. Evaluate on HICO-DET finetuned detection
```Shell
cd Data/ho-rcnn
matlab -r "Generate_detection('finetune'); quit"
cd ../../
```
### Train
0. Down pre-trained Faster R-CNN model weights for initialization
```Shell
bash ./scripts/download_frcnn.sh
```
1. Train on V-COCO
```Shell
bash ./scripts/train_VCOCO.sh
```
2. Train on HICO-DET
```Shell
bash ./scripts/train_HICO.sh
```
### Test
1. Test on V-COCO
```Shell
bash ./scripts/test_VCOCO.sh $APP_ITER_NUMBER $HUMAN_SP_ITER_NUMBER $OBJECT_SP_ITER_NUMBER
```
2. Test on HICO-DET
```Shell
bash ./scripts/test_HICO.sh $APP_ITER_NUMBER $HUMAN_SP_ITER_NUMBER $OBJECT_SP_ITER_NUMBER
```
3. Test on HICO-DET w/ a fine-tined detector
```Shell
bash ./scripts/test_HICO_ft.sh
```
**NOTE:** If you wish the use the same detector for a fair comparison, see [here](DETECTOR.md).
### DRG Pretrained Weights
Download DRG trained weights.
```Shell
bash ./scripts/download_drg_models.sh
```
### Object Detection
For a simple demo, you can try
```Shell
python demo/demo_obj_det.py
```
Currently, we only support Faster R-CNN with ResNet-R50-FPN backbone.
### Citation
If you find this code useful for your research, please consider citing the following papers:
@inproceedings{Gao-ECCV-DRG,
author = {Gao, Chen and Xu, Jiarui and Zou, Yuliang and Huang, Jia-Bin},
title = {DRG: Dual Relation Graph for Human-Object Interaction Detection},
booktitle = {European Conference on Computer Vision},
year = {2020}
}
@inproceedings{gao2018ican,
author = {Gao, Chen and Zou, Yuliang and Huang, Jia-Bin},
title = {iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection},
booktitle = {British Machine Vision Conference},
year = {2018}
}
### Acknowledgement
This code follows the implementation architecture of [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark), [iCAN](https://github.com/vt-vl-lab/iCAN) and [No Frills](https://github.com/BigRedT/no_frills_hoi_det).