YOLOv4-yiny 消融实验

最新推荐文章于 2025-03-18 22:13:15 发布

Gallant Hu

最新推荐文章于 2025-03-18 22:13:15 发布

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分类专栏：目标检测从基础到实践--系列论文讲解

本文链接：https://blog.youkuaiyun.com/weixin_42108090/article/details/114947341

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该实验对比了使用CSPNet和CSPDarknet53的灵活性，并在不同嵌入式GPU上测试了YOLOv4-tiny的性能。结果显示，YOLOv4-tiny在Xavier AGX, Xavier NX, Jetson TX2, Jetson NANO等设备上均能实现实时性能。采用TensorRT FP16加速后，在特定配置下，帧率可达380到1774 FPS。" 70226666,5745352,使用sklearn的GridSearchCV进行参数调优,"['数据挖掘', '机器学习', '模型优化']

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We design an experiment to show how flexible can be if one uses CSPNet with partial functions in computational blocks. We also compare with CSPDarknet53, in which we perform linear scaling down on width and depth.

算法性能

Finally, we put YOLOv4-tiny on different embedded GPUs for testing, including Xavier AGX, Xavier NX, Jetson TX2, Jetson NANO. We also use TensorRT FP32 (FP16 if supported) for testing. All frame rates obtained by different models are listed in Table 14. It is apparent that YOLOv4-tiny can achieve real-time performance no matter which device is used. If we adopt FP16 and batch size 4 to test Xavier AGX and Xavier NX, the frame rat

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