Added YOLOv5 6.0 native support
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133
readme.md
133
readme.md
@@ -6,14 +6,12 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* New documentation for multiple models
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* DeepStream tutorials
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* Native PyTorch support (YOLOv5 and YOLOR)
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* Native YOLOR support
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* Native PP-YOLO support
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* Models benchmark
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* GPU NMS
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* Dynamic batch-size
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**NOTE**: The support for YOLOv5 was removed in this current update. If you want the old repo version, please use the commit 297e0e9 and DeepStream 5.1 requirements.
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### Improvements on this repository
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* Darknet CFG params parser (it doesn't need to edit nvdsparsebbox_Yolo.cpp or another file for native models)
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@@ -24,6 +22,7 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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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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* **YOLOv5 6.0 native support**
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##
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@@ -33,6 +32,7 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* [Tested models](#tested-models)
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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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* [INT8 calibration](#int8-calibration)
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* [Using your custom model](docs/customModels.md)
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@@ -48,9 +48,14 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* [NVIDIA DeepStream SDK 6.0](https://developer.nvidia.com/deepstream-sdk)
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* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
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**For YOLOv5**:
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* [PyTorch >= 1.7.0](https://pytorch.org/get-started/locally/)
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##
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### Tested models
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* [YOLOv5 6.0](https://github.com/ultralytics/yolov5) [[pt]](https://github.com/ultralytics/yolov5/releases/tag/v6.0)
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* [YOLOv4x-Mish](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights)]
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* [YOLOv4-CSP](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights)]
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* [YOLOv4](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights)]
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@@ -265,6 +270,7 @@ deepstream-app -c deepstream_app_config.txt
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**NOTE**: If you want to use YOLOv2 or YOLOv2-Tiny models, change the deepstream_app_config.txt file before run it
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```
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...
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[primary-gie]
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enable=1
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gpu-id=0
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@@ -277,6 +283,127 @@ config-file=config_infer_primary_yoloV2.txt
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##
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### YOLOv5 usage
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#### 1. Copy gen_wts_yoloV5.py from DeepStream-Yolo/utils to ultralytics/yolov5 folder
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#### 2. Open the ultralytics/yolov5 folder
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#### 3. Download pt file from [ultralytics/yolov5](https://github.com/ultralytics/yolov5/releases/tag/v6.0) website (example for YOLOv5n)
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```
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wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5n.pt
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```
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#### 4. Generate cfg and wts files (example for YOLOv5n)
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```
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python3 gen_wts_yoloV5.py -w yolov5n.pt
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```
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#### 5. Copy generated cfg and wts files to DeepStream-Yolo folder
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#### 6. Open DeepStream-Yolo folder
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#### 7. Compile lib
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* 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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* 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 config_infer_primary_yoloV5.txt for your model (example for YOLOv5n)
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```
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[property]
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...
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# 0=RGB, 1=BGR, 2=GRAYSCALE
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model-color-format=0
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# CFG
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custom-network-config=yolov5n.cfg
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# WTS
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model-file=yolov5n.wts
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# Generated TensorRT model (will be created if it doesn't exist)
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model-engine-file=model_b1_gpu0_fp32.engine
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# Model labels file
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labelfile-path=labels.txt
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# Batch size
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batch-size=1
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# 0=FP32, 1=INT8, 2=FP16 mode
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network-mode=0
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# Number of classes in label file
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num-detected-classes=80
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...
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[class-attrs-all]
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# CONF_THRESH
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pre-cluster-threshold=0.25
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```
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#### 8. Change the deepstream_app_config.txt file
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```
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...
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[primary-gie]
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enable=1
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gpu-id=0
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gie-unique-id=1
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nvbuf-memory-type=0
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config-file=config_infer_primary_yoloV5.txt
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```
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#### 9. 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 or custom models, check the gen_wts_yoloV5.py args and use them according to your model
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* Input weights (.pt) file path **(required)**
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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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* Model width **(default = 640 / 1280 [P6])**
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```
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-mw or --width
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```
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* Model height **(default = 640 / 1280 [P6])**
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```
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-mh or --height
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```
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* Model channels **(default = 3)**
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```
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-mc or --channels
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```
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* P6 model
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```
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--p6
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```
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##
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### INT8 calibration
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#### 1. Install OpenCV
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