High-resolution networks (HRNets) for object detection

News

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.

Introduction

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

Performance

ImageNet pretrained models

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

Faster R-CNN

Backbone	#Params	GFLOPs	lr sched	SyncBN	MS train	mAP	model
HRNetV2-W18	26.2M	159.1	1x	N	N	36.1	OneDrive,BaiduDrive(y4hs)
HRNetV2-W18	26.2M	159.1	1x	Y	N	37.2	OneDrive,BaiduDrive(ypnu)
HRNetV2-W18	26.2M	159.1	1x	Y	Y(Default)	37.6	OneDrive,BaiduDrive(ekkm)
HRNetV2-W18	26.2M	159.1	1x	Y	Y(ResizeCrop)	37.6	OneDrive,BaiduDrive(phgo)
HRNetV2-W18	26.2M	159.1	2x	N	N	38.1	OneDrive,BaiduDrive(mz9y)
HRNetV2-W18	26.2M	159.1	2x	Y	Y(Default)	39.4	OneDrive,BaiduDrive(ocuf)
HRNetV2-W18	26.2M	159.1	2x	Y	Y(ResizeCrop)	39.7
HRNetV2-W32	45.0M	245.3	1x	N	N	39.5	OneDrive,BaiduDrive(ztwa)
HRNetV2-W32	45.0M	245.3	1x	Y	Y(Default)	41.0
HRNetV2-W32	45.0M	245.3	2x	N	N	40.8	OneDrive,BaiduDrive(hmdo)
HRNetV2-W32	45.0M	245.3	2x	Y	Y(Default)	42.6	OneDrive,BaiduDrive(k03x)
HRNetV2-W40	60.5M	314.9	1x	N	N	40.4	OneDrive,BaiduDrive(0qda)
HRNetV2-W40	60.5M	314.9	2x	N	N	41.4	OneDrive,BaiduDrive(xny6)

Mask R-CNN

Backbone	lr sched	Mask mAP	Box mAP	model
HRNetV2-W18	1x	34.2	37.3	OneDrive,BaiduDrive(vvc1)
HRNetV2-W18	2x	35.7	39.2	OneDrive,BaiduDrive(x2m7)
HRNetV2-W32	1x	36.8	40.7	OneDrive,BaiduDrive(j2ir)
HRNetV2-W32	2x	37.6	42.1	OneDrive,BaiduDrive(tzkz)

Cascade R-CNN

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

Backbone	lr sched	mAP	model
ResNet-101	20e	42.8	OneDrive,BaiduDrive(bzlg)
HRNetV2-W32	20e	43.7	OneDrive,BaiduDrive(ydd7)

Techniques about multi-scale training

Default

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),

ResizeCrop

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),

Quick start

Environment

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.

Install

Install PyTorch 1.0 following the official instructions
Install mmcv

pip install mmcv

Install pycocotools

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

Install NVIDIA/apex to enable SyncBN

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

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

HRNetV2 pretrained models

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

Datasets

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.

Train (multi-gpu training)

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

Test

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.

Other applications of HRNets (codes and models):

Citation

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{SunZJCXLMWLW19,
  title={High-Resolution Representations for Labeling Pixels and Regions},
  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao 
  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
  journal   = {CoRR},
  volume    = {abs/1904.04514},
  year={2019}
}

Reference

[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.

Acknowledgement

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

GCY190811 / HRNet-Object-Detection