- 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 YOLO11
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
- GitHub - orYx-models yolov8: Computer Vision YOLO v8
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
- autogyro yolo-V8: YOLOv8 in PyTorch gt; ONNX - GitHub
YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite Contribute to autogyro yolo-V8 development by creating an account on GitHub
|