yolov7和yolov8

YOLOv7和YOLOv8是目标检测算法中的两个版本。YOLOv7的网络结构由三部分组成：输入层、backbone和head。与YOLOv5不同的是，YOLOv7将neck层和head层合并为head层，但实际上功能是相同的。backbone用于提取特征，head用于预测。YOLOv7在输入端采用了自适应锚框计算、自适应图片缩放和数据增强方式来提高检测精度。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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- *1* *2* [yolov7和yolov8的创新点详解（附：汇报用的PPT）](https://blog.csdn.net/m0_74890428/article/details/130338162)[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^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *3* [YOLOv8/YOLOv7/YOLOv5系列算法改进【NO.6】增加小目标检测层，提高对小目标的检测效果](https://blog.csdn.net/m0_70388905/article/details/125392908)[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^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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