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yolov5_trt's Introduction

1.运行

BOX_SCORE=True python3 export.py --weight weights/yolov5l.pt --opset 11

生成weights/yolov5l.onnx

2.将ONNX模型转换到TensorRT模型(在Docker zldrobit/tensorrt:20.12-py3-torch-1.8.0)

python3 trt_convert.py --onnx=weights/yolov5l.onnx -o weights/yolov5l_nms.engine --explicit-batch --nms --num-classes 80 --batch_size 16 --ws-digits 30 [--fp16]

3.测试TensorRT的检测结果(在Docker ``zldrobit/tensorrt:20.12-py3-torch-1.8.0`)

python3 detect.py --weights weights/yolov5l_nms.engine --no-nms --num-classes 80
4.测试TensorRT模型在TensorRT Server上的预测结果（在Docker `zldrobit/tensorrtserver:20.02-py3-dynamic-batch-size-batchedNMSPlugin`） 在TensorRT Server部署好生成的*.engine文件，例如：重命名为model.plan ``` python3 trt_client_image_v1_trt.py ```

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