QAHOI: Query-Based Anchors for Human-Object Interaction Detection (paper)

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Deep LearningQAHOI
Overview

QAHOI

QAHOI: Query-Based Anchors for Human-Object Interaction Detection (paper)

Requirements

  • PyTorch >= 1.5.1
  • torchvision >= 0.6.1
pip install -r requirements.txt
  • Compiling CUDA operators
cd ./models/ops
sh ./make.sh
# test
python test.py

Dataset Preparation

Please follow the HICO-DET dataset preparation of GGNet.

After preparation, the data folder as follows:

data
├── hico_20160224_det
|   ├── images
|   |   ├── test2015
|   |   └── train2015
|   └── annotations
|       ├── anno_list.json
|       ├── corre_hico.npy
|       ├── file_name_to_obj_cat.json
|       ├── hoi_id_to_num.json
|       ├── hoi_list_new.json
|       ├── test_hico.json
|       └── trainval_hico.json

Evaluation

Download the model to params folder.

  • We test the model with NVIDIA A6000 GPU, Pytorch 1.9.0, Python 3.8 and CUDA 11.2.
Model Full (def) Rare (def) None-Rare (def) Full (ko) Rare (ko) None-Rare (ko) Download
Swin-Tiny 28.47 22.44 30.27 30.99 24.83 32.84 model
Swin-Base*+ 33.58 25.86 35.88 35.34 27.24 37.76 model
Swin-Large*+ 35.78 29.80 37.56 37.59 31.36 39.36 model

Evaluating the model by running the following command.

--eval_extra to evaluate the spatio contribution.

mAP_default.json and mAP_ko.json will save in current folder.

  • Swin-Tiny
python main.py --resume params/QAHOI_swin_tiny_mul3.pth --backbone swin_tiny --num_feature_levels 3 --use_nms --eval
  • Swin-Base*+
python main.py --resume params/QAHOI_swin_base_384_22k_mul3.pth --backbone swin_base_384 --num_feature_levels 3 --use_nms --eval
  • Swin-Large*+
python main.py --resume params/QAHOI_swin_large_384_22k_mul3.pth --backbone swin_large_384 --num_feature_levels 3 --use_nms --eval

Training

Download the pre-trained swin-tiny model from Swin-Transformer to params folder.

Training QAHOI with Swin-Tiny from scratch.

python -m torch.distributed.launch \
        --nproc_per_node=8 \
        --use_env main.py \
        --backbone swin_tiny \
        --pretrained params/swin_tiny_patch4_window7_224.pth \
        --output_dir logs/swin_tiny_mul3 \
        --epochs 150 \
        --lr_drop 120 \
        --num_feature_levels 3 \
        --num_queries 300 \
        --use_nms

Training QAHOI with Swin-Base*+ from scratch.

python -m torch.distributed.launch \
        --nproc_per_node=8 \
        --use_env main.py \
        --backbone swin_base_384 \
        --pretrained params/swin_base_patch4_window7_224_22k.pth \
        --output_dir logs/swin_base_384_22k_mul3 \
        --epochs 150 \
        --lr_drop 120 \
        --num_feature_levels 3 \
        --num_queries 300 \
        --use_nms

Training QAHOI with Swin-Large*+ from scratch.

python -m torch.distributed.launch \
        --nproc_per_node=8 \
        --use_env main.py \
        --backbone swin_large_384 \
        --pretrained params/swin_large_patch4_window12_384_22k.pth \
        --output_dir logs/swin_large_384_22k_mul3 \
        --epochs 150 \
        --lr_drop 120 \
        --num_feature_levels 3 \
        --num_queries 300 \
        --use_nms

Citation

@article{cjw,
  title={QAHOI: Query-Based Anchors for Human-Object Interaction Detection},
  author={Junwen Chen and Keiji Yanai},
  journal={arXiv preprint arXiv:2112.08647},
  year={2021}
}
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