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
Ultralytics YOLO 🚀. Contribute to ultralytics/ultralytics development by creating an account on GitHub.
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 …
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 …
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
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 …
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 …
hero/YOLOv8: NEW - YOLOv8 in PyTorch > ONNX - 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 …
NEW - YOLOv8 in PyTorch > ONNX > CoreML > TFLite - GitHub
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 …
GitHub - DataXujing/YOLOv8: :fire: Official YOLOv8模型训练和部署
:fire: Official YOLOv8模型训练和部署. Contribute to DataXujing/YOLOv8 development by creating an account on GitHub.