paper

Datasets:
- PASCAL VOC 2007+12
- WiderPerson
- SODA10M (for autonomous vehicles)
- DOTA1.0
(We only provide the train/val/test.txt file we used so that you can reproduce our results. The images & labels can be found on the official websites of these datasets.)

scaled-yolov4

slim neck scaled-yolov4

(Updated July 14th)

git clone https://github.com/AlanLi1997/slim-neck-by-gsconv.git
cd slim-neck-by-gsconv/gsconv-yolov5
pip install requirements.txt
python train.py --cfg models/sm-yolov5s.yaml

(Updated Aug 17th)

git clone https://github.com/AlanLi1997/slim-neck-by-gsconv.git
cd slim-neck-by-gsconv
pip install requirements.txt
cd gsconv-scaled-yolov4
python train.py --cfg models/sm-yolov4-p5.yaml

MS COCO

cd gsconv-yolov5
python val.py --data yourdata.yaml --weights sm-yolov5s.pt --task test

cd gsconv-scaled-yolov4
python val.py --data yourdata.yaml --weights sm-yolov4-p5.pt --task test

• https://github.com/ultralytics/yolov5
• https://github.com/AlexeyAB/darknet/tree/yolov4
• https://github.com/WongKinYiu/PyTorch_YOLOv4
• https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
• https://github.com/d-li14/mobilenetv3.pytorch
• https://github.com/megvii-model/ShuffleNet-Series

@article{li2022slim,
title={Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles},
author={Li, Hulin and Li, Jun and Wei, Hanbing and Liu, Zheng and Zhan, Zhenfei and Ren, Qiliang},
journal={arXiv preprint arXiv:2206.02424},
year={2022}
}

论文

实验数据集:
- PASCAL VOC 2007+12 (通用检测器)
- WiderPerson (用于行人检测)
- SODA10M (用于自动驾驶)
- DOTA1.0 (用于遥感影像目标检测)
(我们只提供我们所使用的训练、验证和测试的文本文档,以便于您复现我们的结果。 被标注的图像和它们的标注文件请您访问相应的数据集官方网站获取。)

scaled-yolov4

slim neck scaled-yolov4

(7月14日更新)

git clone https://github.com/AlanLi1997/slim-neck-by-gsconv.git
cd slim-neck-by-gsconv/gsconv-yolov5
pip install requirements.txt
python train.py --cfg models/sm-yolov5s.yaml

(8月17日更新)

git clone https://github.com/AlanLi1997/slim-neck-by-gsconv.git
cd slim-neck-by-gsconv
pip install requirements.txt
cd gsconv-scaled-yolov4
python train.py --cfg models/sm-yolov4-p5.yaml

MS COCO

cd gsconv-yolov5
python val.py --data yourdata.yaml --weights sm-yolov5s.pt --task test

cd gsconv-scaled-yolov4
python val.py --data yourdata.yaml --weights sm-yolov4-p5.pt --task test

• https://github.com/ultralytics/yolov5
• https://github.com/AlexeyAB/darknet/tree/yolov4
• https://github.com/WongKinYiu/PyTorch_YOLOv4
• https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
• https://github.com/d-li14/mobilenetv3.pytorch
• https://github.com/megvii-model/ShuffleNet-Series

@article{li2022slim,
title={Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles},
author={Li, Hulin and Li, Jun and Wei, Hanbing and Liu, Zheng and Zhan, Zhenfei and Ren, Qiliang},
journal={arXiv preprint arXiv:2206.02424},
year={2022}
}