Yolov7模型训练与部署

最新推荐文章于 2025-02-25 13:44:58 发布

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最新推荐文章于 2025-02-25 13:44:58 发布

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分类专栏：人工智能教程文章标签：深度学习计算机视觉人工智能 YoloV7 TensorRT

本文链接：https://blog.youkuaiyun.com/iamqianrenzhan/article/details/127178904

版权

本文介绍YOLOv7的高效目标检测能力，并分享了从环境搭建、数据集准备到模型训练及部署的全过程。YOLOv7以其卓越的速度与精度在目标检测领域占据领先地位。

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背景

在工业上使用较多的基于深度学习从目标检测算法，那毫无疑问应该是yolo，凭借这效率和精度方面的优势，在一众深度学习目标检测算法中脱颖而出。目前最新的版本是yoloV7，根据yoloV7论文中描述：

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or p