YOLOv4: Optimal Speed and Accuracy of Object Detection · HackerPapers discussions

YOLOv4: Optimal Speed and Accuracy of Object Detection — mentioned by edouard-harris

Thu, 12 May 2022 21:10:33 GMT

That Tim Urban piece is great. It's also an interesting time capsule in terms of which AI problems were and were not considered hard in 2015 (when the post was written). From the post: > Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognize the words but understand the meaning of them? Google is currently spending…

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YOLOv4: Optimal Speed and Accuracy of Object Detection — mentioned by GhettoComputers

Wed, 15 Dec 2021 22:18:29 GMT

You’ll want a breakout board to connect it to motors, or see other projects. It’s possible to make a separate mobile platform for the sensors on the phone and have it gather info. You’re going to want to look for NPUs and probably not GPUs. If you don’t have any idea check this out for inspiration. https://arxiv.org/abs/2004.10934 The DJI founders wanted engineers to be seen as hero’s instead of sports stars. He sponsors engineers to complete here, the prize is their dream job. https://www.robomaster.com/en-US

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YOLOv4: Optimal Speed and Accuracy of Object Detection — mentioned by rocauc

Wed, 10 Jun 2020 15:35:03 GMT

EfficientDet was open sourced March 18 [1], YOLOv4 came out April 23 [2], and now YOLOv5 is out only 48 days later. In our initial look, YOLOv5 is 180% faster, 88% smaller, similarly accurate, and easier to use (native to PyTorch rather thank Darknet) than YOLOv4. [1] https://venturebeat.com/2020/03/18/google-ai-open-sources-ef... [2] https://arxiv.org/abs/2004.10934

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YOLOv4: Optimal Speed and Accuracy of Object Detection — mentioned by rocauc

Thu, 21 May 2020 19:37:14 GMT

YOLOv4 was published[1] on April 23 with COCO weights, but there haven't yet been resources on how to adapt its architecture to your own domain. This post walks through setting up Darknet and training on a custom dataset in Colab. FWIW, in our tests, we saw the highest mAP (89.5) on a new task[2] compared to EfficientDet and YOLOv3. [1] https://arxiv.org/abs/2004.10934 [2] https://public.roboflow.ai/object-detection/bccd

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YOLOv4: Optimal Speed and Accuracy of Object Detection — HN submission

Mon, 27 Apr 2020 13:05:30 GMT

YOLOv4: Optimal Speed and Accuracy of Object Detection

234 points; 54 discussion replies on HN. Paper and source history. A submission is not an endorsement.

YOLOv4: Optimal Speed and Accuracy of Object Detection — HN submission

Fri, 24 Apr 2020 08:22:58 GMT

YOLOv4: Optimal Speed and Accuracy of Object Detection

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