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    What is a yolov4 object detector?A typical object detector is composed of several parts including the input, the backbone, the neck, and the head. The backbone of YOLOv4 is pre-trained on ImageNet and is used to predict classes and bounding boxes of objects. The backbone could be from several models including VGG, ResNet, ResNeXt, or DenseNet.
    Why is yolov4 a good choice?YOLOv4 is designed to provide the optimal balance between speed and accuracy, making it an excellent choice for many applications. YOLOv4 architecture diagram. Showcasing the intricate network design of YOLOv4, including the backbone, neck, and head components, and their interconnected layers for optimal real-time object detection.
    How to train Yolo V4 & V3?Training Yolo v4 (and v3): change [ filters=255] to filters= (classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers. So if classes=1 then should be filters=18. If classes=2 then write filters=21 .
    What are the evaluation results for yolov3 & mixed Yolo Lite for shipdata?Evaluation results for YOLOv3 and Mixed YOLOv3-LITE for ShipData. 4.2.4. Performance Tests Based on Embedded Platform Mixed YOLOv3-LITE was tested with a Jetson AGX Xavier device; the results are shown in Table 10. When inputting an image with a size of 224 × 224, it reached 43 FPS.
  5. YOLOv4 - Ultralytics YOLOv8 Docs

  6. YOLOv4 Tutorial #3 - YOLOv4 on Webcam & Python Code …

  7. GitHub - LinkonBSMRSTU/Real-time-YOLOv4-and-Darknet …

  8. Introduction to the YOLO Family - PyImageSearch

  9. Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection …

  10. FOSDEM - video recordings

  11. yolov5 · PyPI

  12. YOLOv5 | PyTorch

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

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