PeizeSun/OneNet

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

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

Comparisons of different label assignment methods. H and W are height and width of feature map, respectively, K is number of object categories. Previous works on one-stage object detection assign labels by only position cost, such as (a) box IoU or (b) point distance between sample and ground-truth. In our method, however, (c) classification cost is additionally introduced. We discover that classification cost is the key to the success of end-to-end. Without classification cost, only location cost leads to redundant boxes of high confidence scores in inference, making NMS post-processing a necessary component.

Introduction

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

Updates

• (11/12/2020) Higher Performance for OneNet is reported by disable gradient clip.

Comming

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

Models

We provide two models

• dcn is for high accuracy
• nodcn is for easy deployment.

Models are available in Baidu Drive by code nhr8.

Notes

• We observe about 0.3 AP noise.
• The training time and inference time are on 8 NVIDIA V100 GPUs.
• We use the models pre-trained on imagenet using torchvision. And we provide torchvision’s ResNet-18.pkl model. More details can be found in the conversion script.

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}: Towards End-to-End One-Stage Object Detection},
  author  =  {Peize Sun and Yi Jiang and Enze Xie and Zehuan Yuan and Changhu Wang and Ping Luo},
  journal =  {arXiv preprint arXiv: 2012.05780},
  year    =  {2020}
}