• HRNet-Object-Detection is combined into the mmdetection codebase. More results are available at model zoo and the HRNet folder https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet.

• HRNet with an anchor-free detection algorithm FCOS is available at https://github.com/HRNet/HRNet-FCOS

• Multi-scale training available. We've involved SyncBatchNorm and Multi-scale training(We provided two kinds of implementation) in HRNetV2 now! After trained with multiple scales and SyncBN, the detection models obtain better performance. Code and models have been updated already!

• HRNet-Object-Detection on the MaskRCNN-Benchmark codebase is available at https://github.com/HRNet/HRNet-MaskRCNN-Benchmark.

This is the official code of High-Resolution Representations for Object Detection. We extend the high-resolution representation (HRNet) [1] by augmenting the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions, leading to stronger representations. We build a multi-level representation from the high resolution and apply it to the Faster R-CNN, Mask R-CNN and Cascade R-CNN framework. This proposed approach achieves superior results to existing single-model networks on COCO object detection. The code is based on mmdetection

HRNetV2 ImageNet pretrained models are now available! Codes and pretrained models are in HRNets for Image Classification

All models are trained on COCO train2017 set and evaluated on COCO val2017 set. Detailed settings or configurations are in configs/hrnet.

Note: Models are trained with the newly released code and the results have minor differences with that in the paper. Current results will be updated soon and more models and results are comming.

Note: Pretrained HRNets can be downloaded at HRNets for Image Classification

Note: we follow the original paper[2] and adopt 280k training iterations which is equal to 20 epochs in mmdetection.

• Procedure

  1. Select one scale from provided scales randomly and apply it.
  2. Pad all images in a GPU Batch(e.g. 2 images per GPU) to the same size (see pad_size, 1600*1000 or 1000*1600)

• Code

You need to change lines below in config files

data = dict(
    imgs_per_gpu=4,
    workers_per_gpu=8,
    pad_size=(1600, 1024),
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'images/train2017.zip',
        img_scale=[(1600, 1000), (1000, 600), (1333, 800)],
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0.5,
        with_mask=False,
        with_crowd=True,
        with_label=True),

Less memory and less time, this implementation is more efficient compared to the former one

• Procedure

  1. Select one scale from provided scales randomly and apply it.
  2. Crop images to a fixed size randomly if they are larger than the given size.
  3. Pad all images to the same size (see pad_size).

• Code

You need to change lines below in config files

    imgs_per_gpu=2,
    workers_per_gpu=4,
    pad_size=(1216, 800),
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017.zip',
        img_scale=(1200, 800),
        img_norm_cfg=img_norm_cfg,
        size_divisor=1,
        extra_aug=dict(
            rand_resize_crop=dict(
                scales=[[1400, 600], [1400, 800], [1400, 1000]],
                size=[1200, 800]
            )),
        flip_ratio=0.5,
        with_mask=False,
        with_crowd=True,
        with_label=True),

This code is developed using on Python 3.6 and PyTorch 1.0.0 on Ubuntu 16.04 with NVIDIA GPUs. Training and testing are performed using 4 NVIDIA P100 GPUs with CUDA 9.0 and cuDNN 7.0. Other platforms or GPUs are not fully tested.

1. Install PyTorch 1.0 following the official instructions
2. Install mmcv

pip install mmcv

3. Install pycocotools

git clone https://github.com/cocodataset/cocoapi.git \
 && cd cocoapi/PythonAPI \
 && python setup.py build_ext install \
 && cd ../../

4. Install NVIDIA/apex to enable SyncBN

git clone https://github.com/NVIDIA/apex
cd apex
python setup install --cuda_ext

5. Install HRNet-Object-Detection

git clone https://github.com/HRNet/HRNet-Object-Detection.git

cd HRNet-Object-Detection
# compile CUDA extensions.
chmod +x compile.sh
./compile.sh

# run setup
python setup.py install 

# or install locally
python setup.py install --user

For more details, see INSTALL.md

cd HRNet-Object-Detection
# Download pretrained models into this folder
mkdir hrnetv2_pretrained

Please download the COCO dataset from cocodataset. If you use zip format, please specify CocoZipDataset in config files or CocoDataset if you unzip the downloaded dataset.

Please specify the configuration file in configs (learning rate should be adjusted when the number of GPUs is changed).

python -m torch.distributed.launch --nproc_per_node <GPUS NUM> tools/train.py <CONFIG-FILE> --launcher pytorch
# example:
python -m torch.distributed.launch --nproc_per_node 4 tools/train.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py --launcher pytorch

python tools/test.py <CONFIG-FILE> <MODEL WEIGHT> --gpus <GPUS NUM> --eval bbox --out result.pkl
# example:
python tools/test.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py work_dirs/faster_rcnn_hrnetv2p_w18_1x/model_final.pth --gpus 4 --eval bbox --out result.pkl

NOTE: If you meet some problems, you may find a solution in issues of official mmdetection repo or submit a new issue in our repo.

• Human pose estimation
• Semantic segmentation
• Facial landmark detection
• Image classification

If you find this work or code is helpful in your research, please cite:

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal   = {CoRR},
  volume    = {abs/1908.07919},
  year={2019}
}

[1] Deep High-Resolution Representation Learning for Human Pose Estimation. Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. CVPR 2019. download

[2] Cascade R-CNN: Delving into High Quality Object Detection. Zhaowei Cai, and Nuno Vasconcetos. CVPR 2018.

Thanks @open-mmlab for providing the easily-used code and kind help!