yolov7的anchors

对于YOLOv7的anchors，可以使用自动锚点计算工具来重新计算。可以使用引用中提到的autoanchor模块来计算新的anchors。在这个示例中，使用的是一个名为data.yaml的数据集配置文件，计算出的新的anchors会打印输出。同时，参考提供的博客文章也可以了解有关YOLOv7锚点的更多详细信息。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [YOLOv5/v7修改初始锚定框anchor（以及utils文件夹的部分py文件报错）](https://blog.csdn.net/m0_60461719/article/details/127067097)[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^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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相关问题

yolov7 anchors

YOLOv7是一种目标检测算法，anchors是YOLOv7中的一种重要概念，用于生成候选框。anchors是指在图像中预定义的一些框，这些框的大小和宽高比是固定的，通过这些框可以生成不同大小和宽高比的候选框，从而检测出不同大小和形状的目标。

YOLOv7中使用了三种不同大小的anchors，每种大小的anchors对应着不同大小的目标。具体来说，YOLOv7使用了9个anchors，分别对应着3种不同大小的目标。这些anchors的大小和宽高比是通过聚类算法得到的。

yolov5 anchors

YOLOv5 uses anchor boxes to improve object detection accuracy. Anchor boxes are predefined bounding boxes of various sizes and aspect ratios that are placed over the image at different positions. These anchor boxes act as reference points for the network to predict the final bounding boxes for the detected objects.

YOLOv5 uses a predefined set of anchor boxes based on the dataset being used. The anchor boxes are calculated using the k-means clustering algorithm on the ground-truth bounding boxes of the training dataset. The number of anchor boxes is usually set to be the same as the number of classes to be detected.

During training, the network learns to adjust the anchor boxes to better fit the objects in the image. This helps to improve the accuracy of the object detection.