- YOLOv8 README. zh-CN. md at main · Pertical YOLOv8 · GitHub
YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite Contribute to Pertical YOLOv8 development by creating an account on GitHub
- ultralytics docs en models yolov8. md at main - GitHub
YOLOv8 is designed to improve real-time object detection performance with advanced features Unlike earlier versions, YOLOv8 incorporates an anchor-free split Ultralytics head, state-of-the-art backbone and neck architectures, and offers optimized accuracy -speed tradeoff, making it ideal for diverse applications
- GitHub - ultralytics ultralytics: Ultralytics YOLO
Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use They excel at object detection, tracking, instance segmentation, image classification, and pose estimation tasks Find detailed documentation in the
- GitHub - haermosi yolov8: YOLOv8
Ultralytics YOLOv8, developed by Ultralytics, is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image
- Neurallabware yolo_v8: NEW - YOLOv8 in PyTorch - GitHub
Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification
- YOLOv8 README. zh-CN. md at main · RhineAI YOLOv8 · GitHub
NEW - YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite - YOLOv8 README zh-CN md at main · RhineAI YOLOv8
- Releases · ultralytics assets - GitHub
YOLOv8 OBB Models: The introduction of Oriented Bounding Box models in YOLOv8 marks a significant step in object detection, especially for angled or rotated objects, enhancing accuracy and reducing background noise in various applications such as aerial imagery and text detection
- 使用yolov8-pose进行人体关键点检测，通过计算人体各关键点关系进行人体摔倒检测（ncnn框架实现）
以下是一些图片和视频的展示结果，有一些结果推理不正确，理论上是因为yolov8-pose识别人体关键点出错（为了减少训练时间，我只用了一万多张图片进行训练，如果使用所有的关键点检测数据进行训练的话，相信结果一定好很多）。
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