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yolov5

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YOLOv5

TensorRTx inference code base for ultralytics/yolov5.

Contributors

Different versions of yolov5

Currently, we support yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.0

  • For yolov5 v7.0, download .pt from yolov5 release v7.0, git clone -b v7.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v7.0
  • For yolov5 v6.2, download .pt from yolov5 release v6.2, git clone -b v6.2 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v6.2 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v6.2
  • For yolov5 v6.0, download .pt from yolov5 release v6.0, git clone -b v6.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v6.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v6.0.
  • For yolov5 v5.0, download .pt from yolov5 release v5.0, git clone -b v5.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v5.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v5.0.
  • For yolov5 v4.0, download .pt from yolov5 release v4.0, git clone -b v4.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v4.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v4.0.
  • For yolov5 v3.1, download .pt from yolov5 release v3.1, git clone -b v3.1 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v3.1 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v3.1.
  • For yolov5 v3.0, download .pt from yolov5 release v3.0, git clone -b v3.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v3.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v3.0.
  • For yolov5 v2.0, download .pt from yolov5 release v2.0, git clone -b v2.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v2.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v2.0.
  • For yolov5 v1.0, download .pt from yolov5 release v1.0, git clone -b v1.0 https://github.com/ultralytics/yolov5.git and git clone -b yolov5-v1.0 https://github.com/wang-xinyu/tensorrtx.git, then follow how-to-run in tensorrtx/yolov5-v1.0.

Config

  • Choose the YOLOv5 sub-model n/s/m/l/x/n6/s6/m6/l6/x6 from command line arguments.
  • Other configs please check src/config.h

Build and Run

Detection

  1. generate .wts from pytorch with .pt, or download .wts from model zoo
git clone -b v7.0 https://github.com/ultralytics/yolov5.git
git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git
cd yolov5/
wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
cp [PATH-TO-TENSORRTX]/yolov5/gen_wts.py .
python gen_wts.py -w yolov5s.pt -o yolov5s.wts
# A file 'yolov5s.wts' will be generated.
  1. build tensorrtx/yolov5 and run
cd [PATH-TO-TENSORRTX]/yolov5/
# Update kNumClass in src/config.h if your model is trained on custom dataset
mkdir build
cd build
cp [PATH-TO-ultralytics-yolov5]/yolov5s.wts . 
cmake ..
make

./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw]  // serialize model to plan file
./yolov5_det -d [.engine] [image folder]  // deserialize and run inference, the images in [image folder] will be processed.

# For example yolov5s
./yolov5_det -s yolov5s.wts yolov5s.engine s
./yolov5_det -d yolov5s.engine ../images

# For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
./yolov5_det -s yolov5_custom.wts yolov5.engine c 0.17 0.25
./yolov5_det -d yolov5.engine ../images

  1. Check the images generated, _zidane.jpg and _bus.jpg

  2. 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_det_trt.py

// Another version of python script, which is using CUDA Python instead of pycuda.
python yolov5_det_trt_cuda_python.py

Classification

# Download ImageNet labels
wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/imagenet_classes.txt

# Build and serialize TensorRT engine
./yolov5_cls -s yolov5s-cls.wts yolov5s-cls.engine s

# Run inference
./yolov5_cls -d yolov5s-cls.engine ../images

Instance Segmentation

# Build and serialize TensorRT engine
./yolov5_seg -s yolov5s-seg.wts yolov5s-seg.engine s

# Download the labels file
wget -O coco.txt https://raw.githubusercontent.com/amikelive/coco-labels/master/coco-labels-2014_2017.txt

# Run inference with labels file
./yolov5_seg -d yolov5s-seg.engine ../images coco.txt

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 GoogleDrive or BaiduPan pwd: a9wh

  2. unzip it in yolov5/build

  3. set the macro USE_INT8 in src/config.h and make

  4. serialize the model and test

More Information

See the readme in home page.