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YOLO

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Discover objects in photos and videos with YOLO, the innovative object detection system by the University of Washington. Accurate, real-time detection for professionals and beginners alike. Try YOLO today!

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What is YOLO?

YOLO, developed by the esteemed computer vision team at the University of Washington, is an innovative and robust object detection system. It is commonly known as You Only Look Once and serves as a valuable resource for businesses and individuals seeking swift and precise identification of objects in images and videos. By utilizing YOLO's real-time object detection technology, locating desired objects has become more effortless, encompassing a wide range of items such as cars, furniture, facial expressions, and emotions. YOLO's interface is intuitive and user-friendly, catering to users of all expertise levels, be it beginners or experts. Whether you are a professional photographer or an amateur enthusiast, YOLO caters to diverse needs. With its state-of-the-art technology and user-friendly design, YOLO emerges as the ideal tool for object detection, enabling swift and accurate identification in photos and videos.

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Website traffic

  • Monthly visits
    145.45K
  • Avg visit duration
    00:01:20
  • Bounce rate
    71.97%
  • Unique users
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YOLO FQA

  • What is YOLOv3?icon plus
  • How fast is YOLOv3?icon plus
  • What is the mAP of YOLOv3 on COCO test-dev?icon plus
  • How does YOLOv3 compare to other detectors in terms of speed and accuracy?icon plus
  • Can the size of the YOLOv3 model be changed to trade off between speed and accuracy?icon plus

YOLO Use Cases

YOLO is a real-time object detection system that processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev.

YOLOv3 is on par with Focal Loss in terms of mAP measured at .5 IOU, but it is about 4x faster.

YOLOv3 allows you to easily tradeoff between speed and accuracy by changing the size of the model.

YOLOv2 608x608 has a mAP of 48.1 on COCO test-dev.

Tiny YOLO has a mAP of 23.7 on COCO test-dev.

YOLOv3-320 has a mAP of 51.5 on COCO test-dev.

YOLOv3-416 has a mAP of 55.3 on COCO test-dev.

YOLOv3-608 has a mAP of 57.9 on COCO test-dev.

YOLOv3-tiny has a mAP of 33.1 on COCO test-dev.

YOLOv3-spp has a mAP of 60.6 on COCO test-dev.

YOLOv3 can be used for real-time object detection on a webcam or video file.

YOLO can be trained on the VOC or COCO dataset for custom object detection.

YOLOv3 can be used on the Open Images dataset for object detection.

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