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Pytorch implementation of the 'Slim-neck by GSConv: a lightweight-design for real-time detector architectures'

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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.) --- ### An example of comparison on remote sensing images

scaled-yolov4

slim neck scaled-yolov4


Training the custom datasets

1. For GSConv-yolov5

(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

2. For GSConv-scaled_yolov4

(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

Pretrained Checkpoints

MS COCO

Model size
(pixels)
mAPval
0.5:0.95
mAPval
0.5
FPS
T4 b1
FPS
T4 b32
params
(M)
FLOPs
@640 (G)
yolov5n(ultralytics) 640 28.0 45.7 -- -- 1.9 4.5
GSyolov5n 640 28.4(+0.4) 47.0(+1.3) 147 207 1.8 4.0
Model size
(pixels)
mAPval
0.5:0.95
mAPval
0.5
FPS
A40 b1
FPS
A40 b32
params
(M)
FLOPs
@640 (G)
yolov5s 640 35.7 54.3 109 297 7.2 16.4
GSyolov5s 640 36.0(+0.3) 54.2 95 312(+15) 7.0 14.5

Testing the slim-neck detectors

1. For GSConv-yolov5

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

2. For GSConv-scaled-yolov4

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

References

Citation

@article{li2024slim,
title={Slim-neck by GSConv: a lightweight-design for real-time detector architectures},
author={Li, Hulin and Li, Jun and Wei, Hanbing and Liu, Zheng and Zhan, Zhenfei and Ren, Qiliang},
journal={Journal of Real-Time Image Processing},
volume={21},
number={3},
pages={62},
year={2024},
publisher={Springer}
}

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