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release/1.1.0
FastDeploy/examples/vision/classification/yolov5cls
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guxukai 9cd00ad4c5 [Model] Refactoring code of YOLOv5Cls with new model type (#1237) 
* Refactoring code of YOLOv5Cls with new model type

* fix reviewed problem

* Normalize&HWC2CHW -> NormalizeAndPermute

* remove cast()
2023-02-08 11:19:00 +08:00
..
cpp
[Model] Refactoring code of YOLOv5Cls with new model type (#1237)
2023-02-08 11:19:00 +08:00
python
[Model] Refactoring code of YOLOv5Cls with new model type (#1237)
2023-02-08 11:19:00 +08:00
README_CN.md
[Doc]Add English version of documents in examples (#1070)
2023-01-06 09:34:28 +08:00
README.md
[Doc]Update English version of some documents (#1083)
2023-01-09 10:08:19 +08:00

README.md

English | 简体中文

YOLOv5Cls Ready-to-deploy Model

  • YOLOv5Cls v6.2 model deployment is based on YOLOv5 and Pre-trained Models on ImageNet.
    • （1）The *-cls.pt model provided by Official Repository can export the ONNX file using export.py in YOLOv5, then deployment can be conducted;
    • （2）The YOLOv5Cls v6.2 Model trained by personal data should export the ONNX file using export.py in YOLOv5.

Download Pre-trained ONNX Model

For developers' testing, models exported by YOLOv5Cls are provided below. Developers can download them directly. (The model accuracy in the following table is derived from the source official repository)

Model Size Accuracy(top1) Accuracy(top5)
YOLOv5n-cls 9.6MB 64.6% 85.4%
YOLOv5s-cls 21MB 71.5% 90.2%
YOLOv5m-cls 50MB 75.9% 92.9%
YOLOv5l-cls 102MB 78.0% 94.0%
YOLOv5x-cls 184MB 79.0% 94.4%

Detailed Deployment Documents

Release Note