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  2. YOLOv4 is a one-stage object detection model that improves on YOLOv3 with several bags of tricks and modules introduced in the literature. The components section below details the tricks and modules used.
    paperswithcode.com/method/yolov4
    Algorithm Overview: The architecture consists of various parts, broadly they are - The input which comes first and it is basically what we've as our set of training images which will be fed to the network - they are processed in batches in parallel by the GPU. Next are the Backbone and the Neck which do the feature extraction and aggregation.
    iq.opengenus.org/yolov4-model-architecture/
    In summary, YOLOv4 is a series of additions of computer vision techniques that are known to work with a few small novel contributions. The main contribution is to discover how all of these techniques can be combined to play off one another effectively and efficiently for object detection.
    blog.roboflow.com/a-thorough-breakdown-of-yolov4/
     
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