YOLO9000: Better, Faster, Stronger · HackerPapers discussions

YOLO9000: Better, Faster, Stronger — HN submission

Wed, 08 Jul 2020 06:57:55 GMT

YOLO9000: Better, Faster, Stronger

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

YOLO9000: Better, Faster, Stronger — mentioned by lioeters

Fri, 20 Mar 2020 12:56:58 GMT

For others like me who were as amused by the name, YOLO9000, it's a real thing, "a state-of-the-art, real-time object detection system that can detect over 9000 object categories". YOLO9000: Better, Faster, Stronger - https://arxiv.org/abs/1612.08242 (2016)

Paper and discussion history. A mention is not an endorsement.

YOLO9000: Better, Faster, Stronger — mentioned by edshiro

Fri, 24 Nov 2017 07:35:33 GMT

The original YOLO is faster (especially the "Fast YOLO" variant), but is a lot less accurate in its detections: Fast YOLO has 52.7 mean Average Precision @155 FPS vs SSD-300 with 74.3 mAP @59 FPS. But the new YOLO9000[1] architecture seems to be even better than SSD! Would like to try it at some point for sure. [1] YOLO9000: Better, Faster, Stronger: https://arxiv.org/abs/1612.08242

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YOLO9000: Better, Faster, Stronger — mentioned by edshiro

Wed, 08 Nov 2017 11:30:26 GMT

There are quite a few papers that discuss the application R-CNN (Region-based Convolutional Neural Network) for object detection and classification [1-3]. On websites such as Medium, you can find lots of blog posts on object detection using popular frameworks such as Tensorflow (e.g. https://towardsdatascience.com/how-to-train-your-own-object-...). [1] Faster R-CNN: https://arxiv.org/abs/1506.01497 [2] Single Shot Multibox: https://arxiv.org/abs/1512.02325 [3] YOLO9000: https://arxiv.org/abs/1612.08242

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YOLO9000: Better, Faster, Stronger — mentioned by 0xbear

Mon, 21 Aug 2017 05:59:51 GMT

This is not "modern" though, the paper is quite old. He has improved it significantly since then: https://arxiv.org/abs/1612.08242. Best paper honorable mention at CVPR2017.

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YOLO9000: Better, Faster, Stronger — mentioned by iraphael

Sun, 23 Jul 2017 01:07:44 GMT

* Densely Connected Convolutional Networks [0] by Gao Huang, Zhuang Liu, Laurens van der Maaten, & Kilian Q. Weinberger. * Learning from Simulated and Unsupervised Images through Adversarial Training [1] by Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, & Russell Webb CVPR 2017 Best Paper Honorable Mention Awards * Annotating Object Instances with a Polygon-RNN [2] by Lluís Castrejón, Kaustav Kundu, Raquel Urtasun, & Sanja Fidler * YOLO9000: Better, Faster, Stronger [3] by Joseph Redmon & Ali Farhadi CVPR 2017 Best Student Paper Award * Computational Imaging on…

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YOLO9000: Better, Faster, Stronger — mentioned by yamaneko

Sat, 17 Jun 2017 02:07:36 GMT

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 wouldn't be possible to be included in this publication as we can't travel through time yet. [1]: https://arxiv.org/abs/1506.02640 [2]: https://arxiv.org/abs/1612.08242 [3]: https://arxiv.org/abs/1703.06870

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YOLO9000: Better, Faster, Stronger — HN submission

Thu, 29 Dec 2016 10:59:47 GMT

YOLO9000: Better, Faster, Stronger

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.