Awesome

HOI Transformer

Code for CVPR 2021 accepted paper End-to-End Human Object Interaction Detection with HOI Transformer.

This method also won 2nd Place Award in HOI Challenge in Person In Context in CVPR Workshop 2021.

TODO list

Performance

Model	HICO-DET (Full/Rare/NonRare)	V-COCO	HOI-A
Res50	28.92/19.83/31.64	51.15	73.23

Reproduction

We recomend you to setup in the following steps:

1.Clone the repo.

git clone https://github.com/bbepoch/HoiTransformer.git

2.Download the MS-COCO pretrained DETR model.

cd data/detr_coco && bash download_model.sh

3.Download the annotation files for HICO-DET, V-COCO and HOI-A.

cd data && bash download_annotations.sh

4.Download the image files for HICO-DET, V-COCO and HOI-A. Instead, we provide a script to get all of them. A required directory structure is:

    HoiTransformer/
    ├── data/
    │   ├── detr_coco/
    │   ├── hico/
    │   │   ├── eval/
    │   │   └── images/
    │   │       ├── train2015/
    │   │       └── test2015/
    │   ├── hoia/
    │   │   ├── eval/
    │   │   └── images/
    │   │       ├── trainval/
    │   │       └── test/
    │   └── vcoco/
    │       ├── eval/
    │       └── images/
    │           ├── train2014/
    │           └── val2014/
    ├── datasets/
    ├── models/
    ├── tools/
    ├── util/
    ├── engin.py
    ├── main.py
    └── test.py

5.OPTIONAL SETTINGS. When the above subdirectories in 'data' are all ready, you can train a model on any one of the three benchmarks. But before that, we highly recommend you to move the whole folder 'data' to another place on your computer, e.g. '/home/hoi/data', and only put a soft link named 'data' under 'HoiTransformer'.

# Optional but recommended to separate data from code.
mv data /home/hoi/
ln -s /home/hoi/data data

6.Train a model.

# Train on HICO-DET.
python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --epochs=150 --lr_drop=110 --dataset_file=hico --batch_size=2 --backbone=resnet50

# Train on HOI-A.
python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --epochs=150 --lr_drop=110 --dataset_file=hoia --batch_size=2 --backbone=resnet50

# Train on V-COCO.
python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --epochs=150 --lr_drop=110 --dataset_file=vcoco --batch_size=2 --backbone=resnet50

# Training longer can get even better performance.

7.Test a model.

python3 test.py --backbone=resnet50 --batch_size=1 --dataset_file=hico --log_dir=./ --model_path=your_model_path

Annotations

We propose a new annotation format 'ODGT' which is much easier to understand, and we have provided annotation files for all the existing benchmarks, i.e. HICO-DET, HOI-A, V-COCO, so you don't have to know how to get it, just use it. The core structure of 'ODGT' format is:

{
    file_name: XXX.jpg,
    width: image width,
    height: image height,
    gtboxes: [
        {
            box: [x, y, w, h],
            tag: object category name,
        },
        ...
    ],
    hoi: [
        {
            subject_id: human box index in gtboxes,
            object_id: object box index in gtboxes,
            interaction: hoi category name,
        },
        ...
    ],
}

Citation

@inproceedings{zou2021_hoitrans,
  author = {Zou, Cheng and Wang, Bohan and Hu, Yue and Liu, Junqi and Wu, Qian and Zhao, Yu and Li, Boxun and Zhang, Chenguang and Zhang, Chi and Wei, Yichen and Sun, Jian},
  title = {End-to-End Human Object Interaction Detection with HOI Transformer},
  booktitle={CVPR},
  year = {2021},
}

Acknowledgement

We sincerely thank all previous works, especially DETR, PPDM, iCAN, for some of the codes are built upon them.