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

You Only Look Once: Unified, Real-Time Object Detection — mentioned by froobius

Thu, 23 Oct 2025 18:59:20 GMT

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 / have a good training dataset.) We've got a model out there now that we've just seen has put someone's life at risk... Does anyone apart from that company actually know how accurate it is? What it's been trained on? Its false positive rate? If we are going to start rolling out stuff like this, should it not be…

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

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

Wed, 08 Jul 2020 06:58:14 GMT

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

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

You Only Look Once: Unified, Real-Time Object Detection — mentioned by edshiro

Tue, 02 Apr 2019 20:02:31 GMT

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), another popular object detection technique: https://towardsdatascience.com/understanding-ssd-multibox-re... [1] https://arxiv.org/abs/1506.02640

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

You Only Look Once: Unified, Real-Time Object Detection — mentioned by edshiro

Mon, 30 Oct 2017 08:12:11 GMT

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 onto semantic segmentation, I recommend re-implementing this project or more generally solve this problem using a Regional Convolution Neural Network architecture (R-CNN) like Faster R-CNN[1] or YOLO[2] for instance. [1]: https://arxiv.org/abs/1506.01497 [2]: https://arxiv.org/abs/1506.02640

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

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

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

You Only Look Once: Unified, Real-Time Object Detection — mentioned by jimduk

Thu, 17 Mar 2016 13:57:45 GMT

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 the source code and trying to understand how something works in code (often not easy even after reading the papers) - easier than with the big frameworks. Also may be a good place to start for simpler target platforms e.g. embedded. Kudos to pjreddie.

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

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

Thu, 31 Dec 2015 11:05:48 GMT

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

64 points; 8 discussion replies on HN. Paper and source history. A submission is not an endorsement.