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QueryDet-PyTorch

This repository is the official implementation of our paper: QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection, Chenhongyi Yang, Zehao Huang, Naiyan Wang. CVPR 2022

IMPORTANT UPDATE !!!

We have updated the QueryDet repository to make it easier to use. Specifically:

QueryDet now supports newer versions of PyTorch and Detectron2.
You do not need APEX anymore. FP16 training is currently achieved through PyTorch AMP.
QueryDet now supports Spconv 2.1, which can be directly installed using pip.
We have improved the support for the VisDrone dataset.
We have re-orgnized the model configs to make them easier to use.

Setting:

Environment setting:

We tested the new QueryDet with CUDA 10.2 using NVIDIA 2080Ti GPUs. We provide a sample setting-up script as follows:

conda create -n querydet python=3.7 -y
source activate querydet
pip install torch==1.8.1+cu101 torchvision==0.9.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html
python -m pip install detectron2==0.4 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.7/index.html
pip install spconv-cu102==2.1.25

# Clone our repository and have fun with it! 
git clone https://github.com/ChenhongyiYang/QueryDet-PyTorch.git

# OPTIONAL: Install the python evaluation tool for VisDrone
# Reference: https://github.com/tjiiv-cprg/visdrone-det-toolkit-python
cd visdrone_eval
pip install -e .

# OPTIONAL: Install detectron2_backbone if you want to use backbone networks like MobileNet
# Reference: https://github.com/sxhxliang/detectron2_backbone 
git clone https://github.com/sxhxliang/detectron2_backbone.git
cd detectron2_backbone
python setup.py build develop

COCO setting:

You need to set up COCO following the official tutorial of Detectron2.

VisDrone setting:

We provide full support for the VisDrone dataset.

You need to download the VisDrone dataset from its official website.
Unzip and place the downloaded dataset as follows:

QueryDet-PyTorch
|-- data
    |-- visdrone
        |-- VisDrone2019-DET-train
        |   |-- images  
        |   |   |-- ...jpg  # 6471 .jpg files
        |   |-- annotations      
        |       |-- ...txt  # 6471 .txt files
        |-- VisDrone2019-DET-val
            |-- images  
            |   |-- ...jpg  # 548 .jpg files
            |-- annotations      
                |-- ...txt  # 548 .txt files

Pre-process the dataset by running: python visdrone/data_prepare.py --visdrone-root data/visdrone.
The resulting file structure will be as follows:

QueryDet-PyTorch
|-- data
    |-- visdrone
        |-- VisDrone2019-DET-train
        |   |-- images  
        |   |   |-- ...jpg  # 6471 .jpg files
        |   |-- annotations      
        |       |-- ...txt  # 6471 .txt files
        |-- VisDrone2019-DET-val
        |   |-- images  
        |   |   |-- ...jpg  # 548 .jpg files
        |   |-- annotations      
        |       |-- ...txt  # 548 .txt files
        |-- coco_format
            |-- train_images
            |   |-- ...jpg  # 25884 .jpg files
            |-- val_images  
            |   |-- ...jpg  # 548 .jpg files
            |-- annotations
                |-- train_label.json
                |-- val_label.json

After model training, you can evaluate your model by running bash eval_visdrone.sh /path/to/visdrone_infer.json.

Usage

Before training, we recommend you to create a work_dirs directory to store all training results under QueryDet-PyTorch as follows:

QueryDet-PyTorch
|-- work_dirs
|-- ...  # other stuffs

If you do not want to store your training results in another place, you can run ln -s /path/to/your/storage work_dirs to create a symbolic link.

In the following, we will assume you have created such a directory and introduce the training, testing, and evaluating commands.

Training

% train coco RetinaNet baseline
python train_coco.py --config-file configs/coco/retinanet_train.yaml --num-gpu 8 OUTPUT_DIR work_dirs/coco_retinanet

% train coco QueryDet 
python train_coco.py --config-file configs/coco/querydet_train.yaml --num-gpu 8 OUTPUT_DIR work_dirs/coco_querydet

% train VisDrone RetinaNet baseline
python train_visdrone.py --config-file configs/visdrone/retinanet_train.yaml --num-gpu 8 OUTPUT_DIR work_dirs/visdrone_retinanet

% train VisDrone QueryDet
python train_visdrone.py --config-file configs/visdrone/querydet_train.pyaml --num-gpu 8 OUTPUT_DIR work_dirs/visdrone_querydet

Testing

% test coco RetinaNet baseline
python infer_coco.py --config-file configs/coco/retinanet_test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS  work_dirs/coco_retinanet/model_final.pth OUTPUT_DIR work_dirs/model_test

% test coco QueryDet with Dense Inference
python infer_coco.py --config-file configs/coco/querydet_test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS work_dirs/coco_querydet/model_final.pth OUTPUT_DIR work_dirs/model_test

% test coco QueryDet with CSQ
export SPCONV_FILTER_HWIO="1"; python infer_coco.py --config-file configs/coco/querydet_test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS work_dirs/coco_querydet/model_final.pth OUTPUT_DIR work_dirs/model_test MODEL.QUERY.QUERY_INFER True 

% test VisDrone RetinaNet baseline
python infer_coco.py --config-file configs/visdrone/retinanet_test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS  work_dirs/visdrone_retinanet/model_final.pth OUTPUT_DIR work_dirs/model_test

% test VisDrone QueryDet with Dense Inference
python infer_coco.py --config-file configs/visdrone/querydet_test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS work_dirs/visdrone_querydet/model_final.pth OUTPUT_DIR work_dirs/model_test

% test VisDrone QueryDet with CSQ
export SPCONV_FILTER_HWIO="1"; python infer_coco.py --config-file configs/visdrone/querydet_test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS work_dirs/visdrone_querydet/model_final.pth OUTPUT_DIR work_dirs/model_test MODEL.QUERY.QUERY_INFER True

Evaluation

For COCO, Detectron2 will automatically evaluate the result when you run the inference command so you do not need to run any extra command.
For VisDrone, after running an inference command, you will get a result file named visdrone_infer.json in your resulting directory (e.g., work_dirs/model_test in the above commands). Then you have two options to evaluate the result:
- If you have installed the Python evaluation tool, then you can evaluate your result by running bash eval_visdrone.sh work_dirs/model_test/visdrone_infer.json
- If you want to use the official Matlab evaluation tool, you can run python visdrone/json_to_txt.py --out /path/to/result --gt-json data/visdrone/coco_format/annotations/val_label.json --det-json work_dirs/model_test/visdrone_infer.json to convert the result to .txt files for Matlab evaluation.

Citation

@InProceedings{Yang_2022_CVPR_QueryDet,
    author    = {{Yang, Chenhongyi and Huang, Zehao and Wang, Naiyan}},
    title     = {{QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2022}
}