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arXiv:1506.02640cs.CV

You Only Look Once: Unified, Real-Time Object Detection

Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi

Abstract

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.

arXiv comments
Journal reference
DOI
Subjects
cs.CVComputer Vision and Pattern Recognition

On Hacker News

Linked by 4 identifiable commenters in 5 comments across 5 threads, from Mar 2016 to Oct 2025. Found in 2 HN submissions from Dec 2015 to Jul 2020. Open submission discussions.

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Discussed in comments5 threads

  1. Armed police swarm student after AI mistakes bag of Doritos for a weapon 1 comment

    • Stuff like this feels like some company has managed to monetize an open source object detection model like YOLO [1], creating something that could be cobbled together relatively easily, and then sold it as advance AI capabilities. (You'd hope they've have at least fine-tuned it…

      froobiusView on HNParent context

  2. YOLO: Real-Time Object Detection 1 comment

    • YOLO is a very good and approachable object detection technique. I recently re-read the paper for the original YOLO [1] from 2015 and loved the apparent simplicity of this technique. As a shameless plug, I wrote an intuitive guide to understanding SSD (Single Shot Detector)…

      edshiroView on HNParent context

  3. Show HN: Vehicle Detection – using machine learning and computer vision 1 comment

    • Good work! I did this project as well about a week ago. I will shamelessly also share my writings on this project: https://medium.com/towards-data-science/teaching-cars-to-see... . The HOG + SVM method is quite slow and not as accurate as a deep learning approach. Before jumping…

      edshiroView on HNParent context

  4. Supercharge your Computer Vision models with the TensorFlow Object Detection API 1 comment

    • Their repository is pretty neat! It includes three state-of-the-art architectures in object detection: Faster-RCNN, RFCN, and SSD. It is missing YOLO [1][2], though, which shares some similarities with SSD. Another detector is the recently released Mask-RCNN [3], which of course…

      yamanekoView on HNParent context

  5. Darknet: C and CUDA open source neural network framework 1 comment

    • I use this a bit - the You Only Look Once implementation is good & fast (should be - redmon wrote the paper http://arxiv.org/abs/1506.02640) and it's all C. I don't think it's competing to overtake Torch/Caffe/Tensorflow as 'the' framework. What I find it good for is looking at…

      jimdukView on HNParent context

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