yolov7 backbone
时间: 2024-01-31 20:03:12 浏览: 96
基于yolov7改进(源码+图片+说明+报告).rar
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YOLOv7 is a real-time object detection algorithm that uses a deep neural network to predict the bounding boxes and class probabilities of objects in an image. The backbone of YOLOv7 is a convolutional neural network that is used to extract features from the input image.
The backbone of YOLOv7 is a modified version of the EfficientNet architecture, which is a family of convolutional neural networks designed to balance accuracy and efficiency. The EfficientNet architecture uses a combination of convolutional layers with different kernel sizes and depths, as well as a series of scaling factors that control the number of filters in each layer.
In YOLOv7, the backbone is composed of a series of convolutional layers that extract features from the input image at different scales. These features are then fed into a series of detection heads, which predict the bounding boxes and class probabilities of objects in the image.
Overall, the backbone of YOLOv7 plays a critical role in the performance of the algorithm, as it is responsible for extracting meaningful features from the input image that can be used to accurately detect objects.
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