YOLOv3
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A to Z
- A · App
- No
- B · Built By
- University of Washington
- C · CEO & Politics
- N/A (academic research) — No public partisan activity documented
- D · Date
- 2018
- E · Expertise
- Intermediate+
- F · Free Tier
- No free tier
- G · Generates
- Text only
- H · Deployment
- Cloud + Local (open weights)
- I · Inputs
- Text
- J · Jurisdiction
- USA
- K · Key Staffers
- Joseph Redmon (Lead author), Ali Farhadi (Senior author)
- L · Languages
- English-primary
- M · Model Type
- Vision/CV
- N · Safety Issues
- Computer-vision risks: dataset bias, domain shift, privacy concerns in images, and unreliable performance outside benchmark-like settings.
- O · Open Source
- Yes — open weights
- P · Pricing
- Free (open weights; code public domain)
- Q · AIDb Rating
- 4/10 · AIDb v2.0 (rule-based): Capability 4/10 · Currency 4/10 · Access 5/10 · Significance 4/10 → 4.15
- R · What Makes It Special
- The University of Washington's YOLOv3, an incremental update to the YOLO real-time object detector with a slightly bigger, more accurate network: at 320x320 it runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster, and reaches 57.9 mAP@50 in 51 ms on a Titan X versus 57.5 in 198 ms for RetinaNet. All code is online.
- S · Stock / Status
- University
- T · Training Cutoff
- [UNKNOWN]
- U · Users
- [UNKNOWN]
- V · Voice Chat
- No
- W · Weights
- Downloadable
- X · X Handle
- [UNKNOWN]
- Y · Year Founded
- [UNKNOWN]
- Z · Bottom Line
- Open-weights Vision/CV from University of Washington, released 2018.
Data completeness 85% · Last verified 2026-06-11 · Primary source ↗
Cite this
“YOLOv3.” AIDb — The AI Database. Verified 2026-06-11. https://theaidatabase.com/m/yolov3-university-of-washington.html