# YOLOv3 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/yolov3
> Markdown URL: https://aitinkerers.org/technologies/yolov3.md
> Technology record last updated: 2026-05-05T06:41:15Z
> Generated: 2026-09-20T15:43:48Z

YOLOv3 (You Only Look Once, v3) is a single-stage, state-of-the-art object detection architecture utilizing a Darknet-53 backbone for superior speed and accuracy in real-time applications.

YOLOv3, the third major iteration in the YOLO family, significantly improved detection performance, especially for small objects, over its predecessor (YOLOv2). The model uses the Darknet-53 feature extractor: a 53-layer residual network that boosts accuracy without sacrificing speed. Key to its design is the multi-scale prediction mechanism (detecting at three distinct scales: 13x13, 26x26, and 52x52 feature maps). Quantifiable results confirm its efficiency: on the COCO dataset, YOLOv3 achieved 28.2 mAP at 320x320 resolution, running in just 22 ms (demonstrating a speed advantage over models like SSD and RetinaNet).

- Official technology site: https://arxiv.org/abs/1804.02767
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Playing around with Visual AI](https://cincinnati.aitinkerers.org/talks/rsvp_ax1_mnPqHEg)

I'm not gung-ho to do this, but I heard you're looking for speakers. I built a little toy that utilizes A Python package called imageai to detect images in the camera (via a yolo3 model). It then performs some cropping on a detected image and sends it to llava (MS model) for some light image processing with structured output. I would be willing do demo it and walk through the source. If that's something of interest, I could talk about that. Probably would take no more than 20 minutes.

- Event context: AI Tinkerers - Cincinnati Inaugural Meetup (October) — 2024-10-30 — Cincinnati
- Public talk page: https://cincinnati.aitinkerers.org/talks/rsvp_ax1_mnPqHEg

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