duan8/yolov5

yolov5

The Pytorch implementation is ultralytics/yolov5.

Currently, we support yolov5 v1.0(yolov5s only), v2.0, v3.0, v3.1 and v4.0.

• For yolov5 v4.0, please visit yolov5 release v4.0, and use the latest commit of this repo.
• For yolov5 v3.0 and v3.1, please visit yolov5 release v3.0 and yolov5 release v3.1, and checkout commit '6d0f5cb' of this repo.
• For yolov5 v2.0, please visit yolov5 release v2.0, and checkout commit '5cfa444' of this repo.
• For yolov5 v1.0, please visit yolov5 release v1.0, and checkout commit 'f09aa3b' of this repo.

Config

• Choose the model s/m/l/x by NET macro in yolov5.cpp
• Input shape defined in yololayer.h
• Number of classes defined in yololayer.h, DO NOT FORGET TO ADAPT THIS, If using your own model
• FP16/FP32 can be selected by the macro in yolov5.cpp
• GPU id can be selected by the macro in yolov5.cpp
• NMS thresh in yolov5.cpp
• BBox confidence thresh in yolov5.cpp
• Batch size in yolov5.cpp

How to Run, yolov5s as example

1. generate yolov5s.wts from pytorch with yolov5s.pt, or download .wts from model zoo

git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov5.git
// download its weights 'yolov5s.pt'
// copy tensorrtx/yolov5/gen_wts.py into ultralytics/yolov5
// ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py
// go to ultralytics/yolov5
python gen_wts.py
// a file 'yolov5s.wts' will be generated.

2. build tensorrtx/yolov5 and run

// put yolov5s.wts into tensorrtx/yolov5
// go to tensorrtx/yolov5
// ensure the macro NET in yolov5.cpp is s
// update CLASS_NUM in yololayer.h if your model is trained on custom dataset
mkdir build
cd build
cmake ..
make
sudo ./yolov5 -s             // serialize model to plan file i.e. 'yolov5s.engine'
sudo ./yolov5 -d  ../samples // deserialize plan file and run inference, the images in samples will be processed.

3. check the images generated, as follows. _zidane.jpg and _bus.jpg

4. optional, load and run the tensorrt model in python

// install python-tensorrt, pycuda, etc.
// ensure the yolov5s.engine and libmyplugins.so have been built
python yolov5_trt.py

INT8 Quantization

1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images coco_calib from BaiduPan pwd: a9wh

2. unzip it in yolov5/build

3. set the macro USE_INT8 in yolov3.cpp and make

4. serialize the model and test

More Information

See the readme in home page.