zhly0 / OneNet

OneNet: End-to-End One-Stage Object Detection

OneNet: End-to-End One-Stage Object Detection by Classification Cost

Paper

OneNet: End-to-End One-Stage Object Detection by Classification Cost

Comming

• Provide models and logs
• Support to caffe, onnx, tensorRT
• Support to Res18, MobileNet

Models

We provide two models

• R50_dcn is for high accuracy
• R50_nodcn is for easy deployment.

Method	inf_time	train_time	box AP	download
R50_dcn	67 FPS	36h	35.0
R50_nodcn	73 FPS	29h	32.7

Notes

• We observe about 0.3 AP noise.
• The training time and inference time are on 8 NVIDIA V100 GPUs.

Installation

The codebases are built on top of Detectron2 and DETR.

Requirements

• Linux or macOS with Python ≥ 3.6
• PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this
• OpenCV is optional and needed by demo and visualization

Steps

1. Install and build libs

git clone https://github.com/PeizeSun/OneNet.git
cd OneNet
python setup.py build develop

2. Link coco dataset path to OneNet/datasets/coco

mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017

3. Train OneNet

python projects/OneNet/train_net.py --num-gpus 8 \
    --config-file projects/OneNet/configs/onenet.res50.dcn.yaml

4. Evaluate OneNet

python projects/OneNet/train_net.py --num-gpus 8 \
    --config-file projects/OneNet/configs/onenet.res50.dcn.yaml \
    --eval-only MODEL.WEIGHTS path/to/model.pth

5. Visualize OneNet

python demo/demo.py\
    --config-file projects/OneNet/configs/onenet.res50.dcn.yaml \
    --input path/to/images --output path/to/save_images --confidence-threshold 0.4 \
    --opts MODEL.WEIGHTS path/to/model.pth

License

OneNet is released under MIT License.

Citing

If you use OneNet in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:

@article{peize2020onenet,
  title   =  {{OneNet}: End-to-End One-Stage Object Detection},
  author  =  {Peize Sun and Yi Jiang and Zehuan Yuan and Changhu Wang and Ping Luo},
  journal =  {arXiv preprint arXiv: },
  year    =  {2020}
}