yolov7tiny网络模型

YOLOv7Tiny is a variant of the You Only Look Once (YOLO) object detection model. YOLO models are popular in computer vision and deep learning for their real-time object detection capabilities. YOLOv7Tiny is a lightweight version of the YOLOv7 model, designed to have a smaller model size and faster inference speed while sacrificing some accuracy compared to the larger versions.
The YOLOv7Tiny model architecture typically consists of fewer layers and smaller feature maps compared to the original YOLOv7 model. This reduction in complexity allows it to run faster on resource-constrained devices, making it suitable for real-time object detection applications on embedded systems or edge devices.

However, please note that YOLOv7Tiny is not an official version of the YOLO model series. The official versions include YOLOv1, YOLOv2 (YOLO9000), YOLOv3, and YOLOv4. As of my knowledge, there is no official YOLOv7 release. Hence, YOLOv7Tiny might refer to a custom implementation or a modified version developed by someone in the community.