61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
# yolov3-spp
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yolov4 is [here](../yolov4).
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The Pytorch implementation is [ultralytics/yolov3](https://github.com/ultralytics/yolov3). It provides two trained weights of yolov3-spp, `yolov3-spp.pt` and `yolov3-spp-ultralytics.pt`(originally named `ultralytics68.pt`).
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Following tricks are used in this yolov3-spp:
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- Yololayer plugin is different from the plugin used in [this repo's yolov3](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3). In this version, three yololayer are implemented in one plugin to improve speed, codes derived from [lewes6369/TensorRT-Yolov3](https://github.com/lewes6369/TensorRT-Yolov3)
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- Batchnorm layer, implemented by scale layer.
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## Excute:
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```
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1. generate yolov3-spp_ultralytics68.wts from pytorch implementation with yolov3-spp.cfg and yolov3-spp-ultralytics.pt
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git clone https://github.com/wang-xinyu/tensorrtx.git
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git clone https://github.com/ultralytics/yolov3.git
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// download its weights 'yolov3-spp-ultralytics.pt'
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cd yolov3
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cp ../tensorrtx/yolov3-spp/gen_wts.py .
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python gen_wts.py yolov3-spp-ultralytics.pt
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// a file 'yolov3-spp_ultralytics68.wts' will be generated.
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// the master branch of yolov3 should work, if not, you can checkout 4ac60018f6e6c1e24b496485f126a660d9c793d8
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2. put yolov3-spp_ultralytics68.wts into yolov3-spp, build and run
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mv yolov3-spp_ultralytics68.wts ../tensorrtx/yolov3-spp/
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cd ../tensorrtx/yolov3-spp
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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 ./yolov3-spp -s // serialize model to plan file i.e. 'yolov3-spp.engine'
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sudo ./yolov3-spp -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 yolov3-spp.cpp
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- GPU id can be selected by the macro in yolov3-spp.cpp
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- NMS thresh in yolov3-spp.cpp
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- BBox confidence thresh in yolov3-spp.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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