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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.

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

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

We provide two models

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

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

The codebases are built on top of Detectron2 and DETR.

• 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

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

OneNet is released under MIT License.

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}
}