New YOLOv5 conversion and support (>= v2.0)
This commit is contained in:
Marcos Luciano
201
readme.md
201
readme.md
@@ -9,24 +9,25 @@ NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
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* YOLOX support
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* PP-YOLO support
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* YOLOv6 support
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* YOLOv7 support
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* Dynamic batch-size
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### Improvements on this repository
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* Darknet cfg params parser (no need to edit `nvdsparsebbox_Yolo.cpp` or other files)
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* Support for `new_coords`, `beta_nms` and `scale_x_y` params
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* Support for `new_coords` and `scale_x_y` params
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* Support for new models
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* Support for new layers
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* Support for new activations
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* Support for convolutional groups
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* Support for INT8 calibration
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* Support for non square models
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* Support for `reorg`, `implicit` and `channel` layers (YOLOR)
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* YOLOv5 4.0, 5.0, 6.0 and 6.1 support
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* YOLOR support
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* **GPU YOLO Decoder (moved from CPU to GPU to get better performance)** [#138](https://github.com/marcoslucianops/DeepStream-Yolo/issues/138)
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* New documentation for multiple models
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* **YOLOv5 >= 2.0 support**
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* **YOLOR support**
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* **GPU YOLO Decoder** [#138](https://github.com/marcoslucianops/DeepStream-Yolo/issues/138)
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* **GPU Batched NMS** [#142](https://github.com/marcoslucianops/DeepStream-Yolo/issues/142)
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* **New documentation for multiple models**
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* **New YOLOv5 conversion**
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##
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@@ -37,10 +38,10 @@ NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
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* [Benchmarks](#benchmarks)
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* [dGPU installation](#dgpu-installation)
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* [Basic usage](#basic-usage)
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* [YOLOv5 usage](#yolov5-usage)
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* [YOLOR usage](#yolor-usage)
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* [NMS configuration](#nms-configuration)
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* [INT8 calibration](#int8-calibration)
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* [YOLOv5 usage](#docs/YOLOv5.md)
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* [YOLOR usage](#docs/YOLOR.md)
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* [Using your custom model](docs/customModels.md)
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* [Multiple YOLO GIEs](docs/multipleGIEs.md)
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@@ -95,7 +96,7 @@ NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
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### Tested models
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* [Darknet YOLO](https://github.com/AlexeyAB/darknet)
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* [YOLOv5 4.0, 5.0, 6.0 and 6.1](https://github.com/ultralytics/yolov5)
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* [YOLOv5 >= 2.0](https://github.com/ultralytics/yolov5)
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* [YOLOR](https://github.com/WongKinYiu/yolor)
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* [MobileNet-YOLO](https://github.com/dog-qiuqiu/MobileNet-Yolo)
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* [YOLO-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest)
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@@ -448,188 +449,6 @@ config-file=config_infer_primary_yoloV2.txt
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##
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### YOLOv5 usage
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**NOTE**: Make sure to change the YOLOv5 repo version according to your model version before the conversion.
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#### 1. Copy the `gen_wts_yoloV5.py` file from `DeepStream-Yolo/utils` directory to the [YOLOv5](https://github.com/ultralytics/yolov5) folder
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#### 2. Open the YOLOv5 folder
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#### 3. Download the `pt` file from [YOLOv5](https://github.com/ultralytics/yolov5/releases/) repo (example for YOLOv5n 6.1)
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```
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wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
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```
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#### 4. Generate the `cfg` and `wts` files (example for YOLOv5n)
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```
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python3 gen_wts_yoloV5.py -w yolov5n.pt -c models/yolov5n.yaml
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```
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#### 5. Copy the generated `cfg` and `wts` files to the DeepStream-Yolo folder
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#### 6. Open the DeepStream-Yolo folder
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#### 7. Compile the lib
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* DeepStream 6.1 on x86 platform
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```
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CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.0.1 / 6.0 on x86 platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.1 on Jetson platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.0.1 / 6.0 on Jetson platform
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```
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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#### 8. Edit the `config_infer_primary_yoloV5.txt` file according to your model (example for YOLOv5n)
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```
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[property]
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...
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custom-network-config=yolov5n.cfg
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model-file=yolov5n.wts
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...
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```
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#### 9. Edit the `deepstream_app_config.txt` file
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```
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...
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[primary-gie]
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...
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config-file=config_infer_primary_yoloV5.txt
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```
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#### 10. Run
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```
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deepstream-app -c deepstream_app_config.txt
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```
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**NOTE**: For YOLOv5 P6, check the `gen_wts_yoloV5.py` file args and set them according to your model.
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* Input weights (.pt) file path
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```
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-w or --weights
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```
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* Input cfg (.yaml) file path
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```
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-c or --yaml
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```
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* Inference size [size] or [height , weight]
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Default: 640 / 1280 (if --p6)
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```
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-s or --size
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```
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* Example for 1280
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```
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-s 1280
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```
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or
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```
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-s 1280 1280
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```
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##
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### YOLOR usage
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#### 1. Copy the `gen_wts_yolor.py` file from `DeepStream-Yolo/utils` directory to the [YOLOR](https://github.com/WongKinYiu/yolor) folder
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#### 2. Open the YOLOR folder
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#### 3. Download the `pt` file from [YOLOR](https://github.com/WongKinYiu/yolor) repo
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#### 4. Generate the `cfg` and `wts` files (example for YOLOR-CSP)
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```
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python3 gen_wts_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg
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```
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#### 5. Copy the generated `cfg` and `wts` files to the DeepStream-Yolo folder
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#### 6. Open the DeepStream-Yolo folder
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#### 7. Compile the lib
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* DeepStream 6.1 on x86 platform
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```
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CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.0.1 / 6.0 on x86 platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.1 on Jetson platform
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```
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CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
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```
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* DeepStream 6.0.1 / 6.0 on Jetson platform
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```
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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#### 8. Edit the `config_infer_primary_yolor.txt` file according to your model (example for YOLOR-CSP)
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```
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[property]
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...
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custom-network-config=yolor_csp.cfg
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model-file=yolor_csp.wts
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...
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```
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#### 9. Edit the `deepstream_app_config.txt` file
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```
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...
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[primary-gie]
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...
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config-file=config_infer_primary_yolor.txt
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```
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#### 10. Run
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```
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deepstream-app -c deepstream_app_config.txt
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```
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##
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### NMS Configuration
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To change the `iou-threshold`, `score-threshold` and `topk` values, modify the `config_nms.txt` file and regenerate the model engine file.
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