56 lines
1.7 KiB
Markdown
56 lines
1.7 KiB
Markdown
# yolov5
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The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
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I made a copy of [yolov5s.pt(google drive)](https://drive.google.com/drive/folders/1Yaamfa-t_V3ImxYRBESqGzy7k4Arlt95?usp=sharing). Just in case the yolov5 model updated.
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## How to Run
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```
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1. generate yolov5s.wts from pytorch implementation with yolov5s.pt
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git clone https://github.com/wang-xinyu/tensorrtx.git
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git clone https://github.com/ultralytics/yolov5.git
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// download its weights 'yolov5s.pt'
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cd yolov5
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cp ../tensorrtx/yolov5s/gen_wts.py .
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python gen_wts.py
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// a file 'yolov5s.wts' will be generated.
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2. put yolov5s.wts into yolov5, build and run
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mv yolov5s.wts ../tensorrtx/yolov5/
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cd ../tensorrtx/yolov5
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mkdir build
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cd build
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cmake ..
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make
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sudo ./yolov5s -s // serialize model to plan file i.e. 'yolov5s.engine'
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sudo ./yolov5s -d ../samples // deserialize plan file and run inference, the images in samples will be processed.
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3. check the images generated, as follows. _zidane.jpg and _bus.jpg
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```
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/78247927-4d9fac00-751e-11ea-8b1b-704a0aeb3fcf.jpg">
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</p>
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/78247970-60b27c00-751e-11ea-88df-41473fed4823.jpg">
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</p>
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## Config
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- Input shape defined in yololayer.h
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- Number of classes defined in yololayer.h
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- FP16/FP32 can be selected by the macro in yolov5s.cpp
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- GPU id can be selected by the macro in yolov5s.cpp
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- NMS thresh in yolov5s.cpp
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- BBox confidence thresh in yolov5s.cpp
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- Batch size in yolov5s.cpp
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## More Information
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See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
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