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Pytorch implementation of the 'Slim-neck by GSConv: a lightweight-design for real-time detector architectures'
| Date | Stars |
|---|---|
| 2026-07-24 | 254 |
| 2026-07-25 | 254 |
| 2026-07-28 | 254 |
| 2026-07-30 | 254 |
| 2026-08-06 | 254 |
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# [Slim-neck by GSConv: a lightweight-design for real-time detector architectures](https://link.springer.com/epdf/10.1007/s11554-024-01436-6?sharing_token=PnoS2pcyoX3DO53D6AhA1ve4RwlQNchNByi7wbcMAY6DXldzc1NE_k9ohttAZ9oJUMtRgKGzJKgCt4El9qua7_WrXHOS37Upe7OapcdpypW8JiIUpjiGE47a_4DijGzo3ayyfy9W1GXDjVBXCuzym29CD7lXTbN7qA1TlMK1vZU=)
### 🎉🎉🎉NEW WORK! -> [ECCV2024] [Rethinking Features-Fused-Pyramid-Neck for Object Detection](https://github.com/AlanLi1997/rethinking-fpn)
### 🎉🎉🎉NEWNEW WORK! -> [ESWA2025] [A Biologically Inspired Separable Learning Vision Model for Real-time Traffic Object Perception in Dark](https://doi.org/10.1016/j.eswa.2025.129529)
[English](README.md) | [简体中文](README.zh-CN.md)
<br>
<p align="center">
<img src="gsconvdet.png" alt="" width="800" />
</p>
Datasets:
<br /> - PASCAL VOC 2007+12
<br /> - WiderPerson
<br /> - SODA10M (for autonomous vehicles)
<br /> - DOTA1.0
<br />(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
<p align="center">
<img src="remote-scaledv4.jpg" alt="" width="800" />
</p>
slim neck scaled-yolov4
<p align="center">
<img src="sm-remote-scaledv4.jpg" alt="" width="800" />
</p>
---
## 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<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |FPS<br><sup>T4 b1<br> |FPS<br><sup>T4 b32<br> |params<br><sup>(M) |FLOPs<br><sup>@640 (G)
|--- |--- |--- |--- |--- |--- |--- |---
|[yolov5n(ultralytics)](https://objects.githubusercontent.com/github-production-release-asset-2e65be/264818686/3444cd1f-277c-414f-bdc9-3ac8ed6062df?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221024%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221024T112402Z&X-Amz-Expires=300&X-Amz-Signature=93c777453bc3699dc5da551f5d6ea052bc34d4d0c67dd7400629780ddb2d8054&X-Amz-SignedHeaders=host&actor_id=80144976&key_id=0&repo_id=264818686&response-content-disposition=attachment%3B%20filename%3Dyolov5n.pt&response-content-type=application%2Foctet-stream) |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<br><sup>(pixels)** |**mAP<sup>val<br>0.5:0.95** |**mAP<sup>val<br>0.5** |**FPS<br><sup>A40 b1<br>** |**FPS<br><sup>A40 b32<br>** |**params<br><sup>(M)** |**FLOPs<br><sup>@640 (G)**
|[yolov5s](https://raw.githubusercontent.com/AlanLi1997/slim-neck-by-gsconv/master/pre_trained_weights/yolov5s.pt) |640 |35.7 |**54.3** |**109** |297|7.2|16.4
|[GSyolov5s](https://raw.githubusercontent.com/AlanLi1997/slim-neck-by-gsconv/master/pre_trained_weights/GSyolov5s.pt) |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
- 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
- Excerpt of 4,513 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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