models/yolo26/ #22214
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Nice! Great to see the continued progress. Please can you add comparisons with Roboflow's RF-DETR? Seems if you believe Roboflow's numbers that may be significant competition once it gets more mature, particularly given it's more permissive licensing |
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👋 Hello, thank you for starting a discussion about YOLO26 🚀! For an overview and usage guidance, we recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered:
Note: This is an automated response to help you get started quickly. An Ultralytics engineer will also assist you here soon 🙂 If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it: If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results:
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EnvironmentsYOLO may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLO Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit. |
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·dasdf |
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Great! When do you release the models? |
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Hi! Incredible job and awesome surprise! Is there a possibility that YOLO26 will be released in October? Also, is there any group on Discord or anyother platform that I can join to better understand how the work develops and also be useful? Thanks |
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models/yolo26/
YOLO26 from Ultralytics delivers faster, simpler, end-to-end NMS-free object detection optimized for edge and low-power devices.
https://docs.ultralytics.com/models/yolo26/
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