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yolov3-spp/README.md
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Currently this is supporting dynamic input shape, if you want to use non-dynamic version, please checkout commit [659fd2b](https://github.com/wang-xinyu/tensorrtx/commit/659fd2b23482197b19dccf746a5a3dbff1611381).
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`).
The Pytorch implementation is [ultralytics/yolov3 archive branch](https://github.com/ultralytics/yolov3/tree/archive). It provides two trained weights of yolov3-spp, `yolov3-spp.pt` and `yolov3-spp-ultralytics.pt`(originally named `ultralytics68.pt`).
## Config

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yolov3-tiny/README.md
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# yolov3-tiny
The Pytorch implementation is [ultralytics/yolov3](https://github.com/ultralytics/yolov3).
The Pytorch implementation is [ultralytics/yolov3 archive branch](https://github.com/ultralytics/yolov3/tree/archive).
## Excute:

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yolov3/README.md
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# yolov3
The Pytorch implementation is [ultralytics/yolov3](https://github.com/ultralytics/yolov3). It provides two trained weights of yolov3, `yolov3.weights` and `yolov3.pt`
The Pytorch implementation is [ultralytics/yolov3 archive branch](https://github.com/ultralytics/yolov3/tree/archive). It provides two trained weights of yolov3, `yolov3.weights` and `yolov3.pt`
This branch is using tensorrt7 API, there is also a yolov3 implementation using tensorrt4 API, go to [branch trt4/yolov3](https://github.com/wang-xinyu/tensorrtx/tree/trt4/yolov3), which is using [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3).

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yolov4/README.md
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# yolov4
The Pytorch implementation is from [ultralytics/yolov3](https://github.com/ultralytics/yolov3). It can load yolov4.cfg and yolov4.weights(from AlexeyAB/darknet).
The Pytorch implementation is from [ultralytics/yolov3 archive branch](https://github.com/ultralytics/yolov3/tree/archive). It can load yolov4.cfg and yolov4.weights(from AlexeyAB/darknet).
## Config

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yolov5/README.md
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2. build tensorrtx/yolov5 and run
```
cp {ultralytics}/yolov5/yolov5s.wts {tensorrtx}/yolov5/
cd {tensorrtx}/yolov5/
// update CLASS_NUM in yololayer.h if your model is trained on custom dataset
mkdir build
cd build
cp {ultralytics}/yolov5/yolov5s.wts {tensorrtx}/yolov5/build
cmake ..
make
sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file