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  1. 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.

  2. 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 …

  3. 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 …

  4. 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 …

  5. 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 …

  6. 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 …

  7. yolov8/README.zh-CN.md at main · ljwdust/yolov8 · GitHub

    YOLOv8 设计快速、准确且易于使用,使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。 我们希望这里的资源能帮助您充分利用 YOLOv8。

  8. autogyro/yolo-V8: YOLOv8 in PyTorch > ONNX - GitHub

    YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite. Contribute to autogyro/yolo-V8 development by creating an account on GitHub.

  9. 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 …

  10. YOLOv8 using TensorRT accelerate - GitHub

    YOLOv8 using TensorRT accelerate ! Contribute to triple-Mu/YOLOv8-TensorRT development by creating an account on GitHub.