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Marcos Luciano
2023-05-19 03:05:43 -03:00
parent 68f762d5bd
commit 07feae9509
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docs/YOLOv5.md
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@@ -1,6 +1,6 @@
# YOLOv5 usage
**NOTE**: You can use the main branch of the YOLOv5 repo to convert all model versions.
**NOTE**: You can use the master branch of the YOLOv5 repo to convert all model versions.
**NOTE**: The yaml file is not required.
@@ -20,30 +20,31 @@
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip3 install -r requirements.txt
pip3 install onnx onnxsim onnxruntime
```
**NOTE**: It is recommended to use Python virtualenv.
#### 2. Copy conversor
Copy the `gen_wts_yoloV5.py` file from `DeepStream-Yolo/utils` directory to the `yolov5` folder.
Copy the `export_yoloV5.py` file from `DeepStream-Yolo/utils` directory to the `yolov5` folder.
#### 3. Download the model
Download the `pt` file from [YOLOv5](https://github.com/ultralytics/yolov5/releases/) releases (example for YOLOv5s 6.1)
Download the `pt` file from [YOLOv5](https://github.com/ultralytics/yolov5/releases/) releases (example for YOLOv5s 7.0)
```
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
```
**NOTE**: You can use your custom model, but it is important to keep the YOLO model reference (`yolov5_`) in you `cfg` and `weights`/`wts` filenames to generate the engine correctly.
**NOTE**: You can use your custom model.
#### 4. Convert model
Generate the `cfg` and `wts` files (example for YOLOv5s)
Generate the ONNX model file (example for YOLOv5s)
```
python3 gen_wts_yoloV5.py -w yolov5s.pt
python3 export_yoloV5.py -w yolov5s.pt --simplify
```
**NOTE**: To convert a P6 model
@@ -75,7 +76,7 @@ or
#### 5. Copy generated files
Copy the generated `cfg` and `wts` files to the `DeepStream-Yolo` folder.
Copy the generated ONNX model file to the `DeepStream-Yolo` folder.
##
@@ -128,11 +129,13 @@ Edit the `config_infer_primary_yoloV5.txt` file according to your model (example
```
[property]
...
custom-network-config=yolov5s.cfg
model-file=yolov5s.wts
onnx-file=yolov5s.onnx
model-engine-file=yolov5s.onnx_b1_gpu0_fp32.engine
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYolo
...
```
##