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Big update

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This commit is contained in:
Marcos Luciano
2023-05-19 03:05:43 -03:00
parent 68f762d5bd
commit 07feae9509
86 changed files with 1523 additions and 5223 deletions
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docs/YOLOv7.md
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@@ -18,13 +18,14 @@
git clone https://github.com/WongKinYiu/yolov7.git
cd yolov7
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_yoloV7.py` file from `DeepStream-Yolo/utils` directory to the `yolov7` folder.
Copy the `export_yoloV7.py` file from `DeepStream-Yolo/utils` directory to the `yolov7` folder.
#### 3. Download the model
@@ -34,18 +35,18 @@ Download the `pt` file from [YOLOv7](https://github.com/WongKinYiu/yolov7/releas
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt
```
**NOTE**: You can use your custom model, but it is important to keep the YOLO model reference (`yolov7_`) in you `cfg` and `weights`/`wts` filenames to generate the engine correctly.
**NOTE**: You can use your custom model.
#### 4. Reparameterize your model
[YOLOv7](https://github.com/WongKinYiu/yolov7/releases/) and it's variants can't be directly converted to engine file. Therefore, you will have to reparameterize your model using the code [here](https://github.com/WongKinYiu/yolov7/blob/main/tools/reparameterization.ipynb). Make sure to convert your checkpoints in yolov7 repository, and then save your reparmeterized checkpoints for conversion in the next step.
[YOLOv7](https://github.com/WongKinYiu/yolov7/releases/) and its variants cannot be directly converted to engine file. Therefore, you will have to reparameterize your model using the code [here](https://github.com/WongKinYiu/yolov7/blob/main/tools/reparameterization.ipynb). Make sure to convert your custom checkpoints in yolov7 repository, and then save your reparmeterized checkpoints for conversion in the next step.
#### 5. Convert model
Generate the `cfg` and `wts` files (example for YOLOv7)
Generate the ONNX model file (example for YOLOv7)
```
python3 gen_wts_yoloV7.py -w yolov7.pt
python3 export_yoloV7.py -w yolov7.pt --simplify
```
**NOTE**: To convert a P6 model
@@ -77,7 +78,7 @@ or
#### 6. 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.
##
@@ -130,11 +131,13 @@ Edit the `config_infer_primary_yoloV7.txt` file according to your model (example
```
[property]
...
custom-network-config=yolov7.cfg
model-file=yolov7.wts
onnx-file=yolov7.onnx
model-engine-file=yolov7.onnx_b1_gpu0_fp32.engine
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYolo
...
```
##