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  1. AILab-CVC/YOLO-World - GitHub

    • This repo contains the PyTorch implementation, pre-trained weights, and pre-training/fine-tuning code for YOLO-World.
      •YOLO-World is pre-trained on large-scale datas… See more


    Tianheng Cheng2,3,*, Lin Song1,📧,*, Yixiao Ge1,🌟,2, Wenyu Liu3, Xinggang Wang3,📧, Ying Shan1,2
    * … See more


    🔥[2024-2-18]: We thank @Skalskip92 for developing the wonderful segmentation demo via connecting YOLO-World and EfficientSAM. You can try it now at the 🤗 HuggingFa… See more


    YOLO-World is under active development and please stay tuned ☕️!
    •Gradio demo!
    •Complete documents for pre-training YOLO-World.… See more


    The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories l… See more

    Main Results

    Zero-shot Inference on LVIS dataset
    NOTE: 1.The evaluation results of APfixed are tested on LVIS minival with fixed AP. 2.The evaluation results of APmini are tested on L… See more

    Getting started

    1. Installation
    YOLO-World is developed based on torch==1.11.0 mmyolo==0.6.0 and mmdetection==3.0.0.
    2. Preparin… See more

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  2. ultralytics/ultralytics: NEW - YOLOv8 in PyTorch - GitHub

  3. People also ask
    What is modelbit & yolov8?Modelbit is an MLOps platform that lets you train and deploy any ML model, from any Python environment, with a few lines of code. YOLOv8 is the latest iteration of the YOLO (You Only Look Once) algorithm, renowned for its object detection capabilities.
    Why is Yolo a breakthrough in object detection?YOLO (you only look once) was a breakthrough in the object detection field as it was the first single-stage object detector approach that treated detection as a regression problem. The detection architecture only looked once at the image to predict the location of the objects and their class labels.
    What is yolov4?YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and YOLOv3 as the detection head. This design allows YOLOv4 to perform object detection at an impressive speed, making it suitable for real-time applications.
    What is yolov3u?YOLOv3u maintains the same backbone and neck architecture as YOLOv3 but with the updated detection head from YOLOv8. YOLOv3: Introduced the use of three different scales for detection, leveraging three different sizes of detection kernels: 13x13, 26x26, and 52x52. This significantly improved detection accuracy for objects of different sizes.
  4. Releases · ultralytics/yolov3 · GitHub

  5. YOLOv4 - Ultralytics YOLOv8 Docs

  6. Home - Ultralytics YOLOv8 Docs

  7. YOLO v8 Model Guide - Real-Time Object Detection Model …

  8. Introduction to the YOLO Family - PyImageSearch

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  11. YOLOv3 - Ultralytics YOLOv8 Docs

  12. GitHub - avanetten/yoltv4: Better, faster, peering over yonder ...

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  16. Models Supported by Ultralytics - Ultralytics YOLOv8 Docs

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  19. kiyoshiiriemon/yolov4_darknet: YOLOv4 - GitHub

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  21. model - Ultralytics YOLOv8 Docs

  22. yolobit · GitHub Topics · GitHub

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