ai-基于yolov5实现的人脸检测算法-项目实战.zip

English | [简体中文](readme_CN.md) # Overall process ## 1.Pytorch->TensorRT ```shell python export.py --weights "torch's path" --onnx2trt --fp16_trt ``` ## 2.TensorRT inference ```shell python torch2trt/main.py --trt_path "trt's path" ``` Image preprocessing -> TensorRT inference -> visualization # Time-consuming comparison | Backbone |Pytorch(ms) |TensorRT_FP16(ms) | |:---:|:----:|:----:| |yolov5n-0.5| 7.7 | 2.1 | |yolov5n-face| 7.7 | 2.4 | |yolov5s-face| 5.6 | 2.2 | |yolov5m-face| 9.9 | 3.3 | |yolov5l-face| 15.9 | 4.5 | > Pytorch=1.10.0+cu102 TensorRT=8.2.0.6 Hardware=rtx2080ti ```shell python torch2trt/speed.py --torch_path "torch's path" --trt_path "trt's path" ``` # Visualization <table> <tr> <th>yolov5n-0.5</th> <th>yolov5n-face</th> </tr> <tr> <td><img src="./imgs/yolov5n-0.5.jpg" /></td> <td><img src="./imgs/yolov5n-face.jpg" /></td> </tr> </table> <table> <tr> <th>yolov5s-face</th> <th>yolov5m-face</th> <th>yolov5l-face</th> </tr> <tr> <td><img src="./imgs/yolov5s-face.jpg" /></td> <td><img src="./imgs/yolov5m-face.jpg" /></td> <td><img src="./imgs/yolov5l-face.jpg" /></td> </tr> </table>