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ultralytics 8.3.78 new YOLO12 models (#19325)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/models/yolov7.md
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@ -69,7 +69,7 @@ If we compare YOLOv7-X with 114 fps inference speed to YOLOv5-L (r6.1) with 99 f
## Overview
Real-time object detection is an important component in many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
Real-time object detection is an important component in many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) systems, including multi-[object tracking](https://www.ultralytics.com/glossary/object-tracking), autonomous driving, [robotics](https://www.ultralytics.com/glossary/robotics), and [medical image analysis](https://www.ultralytics.com/glossary/medical-image-analysis). In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
Unlike traditional real-time object detectors that focus on architecture optimization, YOLOv7 introduces a focus on the optimization of the training process. This includes modules and optimization methods designed to improve the accuracy of object detection without increasing the inference cost, a concept known as the "trainable bag-of-freebies".