PaddlePaddle / PaddlePaddle/FastDeploy
PyTorch版YOLOv5使用PaddleSlim自动压缩后的模型，可以在移动端部署么？
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- Dominant language
- Python
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Description
使用 PaddleSlim/example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml 压缩的模型，想部署在移动端，有可以参考的Demo么？
PicoDet在精度方面还是不理想，故换用了YOLOv5，但是不知道要怎么部署到移动端，官方给的Demo基本上都是关于PicoDet的。
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with PaddleSlim/example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml and review FastDeploy’s existing mobile PicoDet demos. Determine whether the compressed PyTorch YOLOv5 model is supported by the mobile deployment path, then document the required process and provide a runnable reference demo if one exists.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, mobile
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100