yolov8 推理代码

引用\[1\]: YOLOv8在输出shape和之前的YOLO系列模型有较大差异。它取消了anchor，因此总的bbox数降低三倍，并取消了bbox的置信度，将bbox置信度与分类融合。\[1\]引用\[2\]: YOLOv8的推理代码主要包括以下几个方面的优化：Reduced Precision（减少精度）、Layer and Tensor Fusion（层和张量融合）、Kernel Auto-Tuning（自动调优）、Dynamic Tensor Memory（动态张量内存）、Multi-Stream Execution（多流执行）和Time Fusion（时间融合）。\[2\]引用\[3\]: YOLOv8的预训练模型为pytorch的pt格式，可以通过以下代码将其转换为onnx格式：import torch net = torch.load('yolov8n.pt', map_location='cpu') net.eval() dummpy_input = torch.randn(1, 3, 640, 640) torch.onnx.export(net, dummpy_input, 'yolov8n.onnx', export_params=True, input_names=\['input'\], output_names=\['output'\])\[3\]
#### 引用[.reference_title]
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- *2* [睿智的目标检测——YOLOv8-Pose的TensorRT推理](https://blog.csdn.net/weixin_43293172/article/details/131811576)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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