--- title: "YOLO26" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6ef25cc2-4ee1-4222-97ad-21466d3c1efd" updated: "2026-11-12" --- # YOLO26 > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds objects in images and video with the Ultralytics YOLO model family, which also covers segmentation, classification, depth, pose and tracking. For developers building computer vision apps. YOLO26 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Ultralytics ## About ### What it is YOLO26 is an object detection model from Ultralytics, listed on Hugging Face for the ultralytics library. The company builds YOLO models for computer vision work, and the page says they cover detection, several kinds of segmentation, classification, depth, pose and tracking across video frames. ### Who it suits It suits developers and researchers who build vision features and want a fast, widely used detector with documentation in several languages. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | App developers | Add object detection to an app that reads photos or video | Marking items found in an uploaded picture | | Computer vision research | Researchers | Use it as a baseline and compare other detectors against it | Testing a new dataset on the same task | | Video analysis | Engineers | Follow detected objects from one frame to the next | Tracking a moving object in a clip | | Robotics and devices | Hardware teams | Run a detector on camera input | Spotting objects in a live camera feed | You can skip it if you need a chat model or text generation, since this one works on images and video. ### Where to use it - Python projects that use the ultralytics library - Windows, macOS or Linux with Python, to download the files - Vision pipelines that take photos or video frames as input | Use case | Who | What they get back | | --- | --- | --- | | Object detection | Developers | Boxes and labels for objects in an image | | Segmentation | Researchers | Outlines of objects at the pixel level | | Pose estimation | Engineers | Body or object keypoints for each detection | | Tracking | Video teams | Detected objects followed across frames | ### How to set it up You need Python and the Hugging Face download tool. - Python on Windows, macOS or Linux - The huggingface\_hub package - The ultralytics library for running the model 1. Install huggingface\_hub. 2. Download the model files to your computer. 3. Follow the model card and the Ultralytics documentation to load them in the ultralytics library. ```sh pip install -U huggingface_hub hf download Ultralytics/YOLO26 ``` ### How to use it The record gives the download step, and the model card points to the Ultralytics documentation for running the model. After the download you load the files with the ultralytics library and pick the task you need. ```sh hf download Ultralytics/YOLO26 ``` ### Example You download the files, load the model in a Python project and pass it a photo. For a detection task you would expect boxes and class labels for the objects it finds, as the card describes for its detection work. ### For newcomers and experts If you are new to computer vision, an object detector looks at a picture and draws boxes around the things it recognizes. A first small try is to download the files and run one photo through the library as the documentation shows. For people who know the field, the family spans detection, instance and semantic segmentation, classification, depth, pose and oriented boxes, plus tracking over video. The card gives no speed or accuracy figures here, so check the paper it links before you compare it with other detectors. Photo or video frame goes in → The YOLO26 model finds objects → Task output is boxes, masks or poses → Tracking links objects across frames ### Good to know The card text visible here is mostly links and badges, so the practical run steps live in the Ultralytics documentation. - License: AGPL-3.0 - Cost to run: none named, though commercial use goes through a paid Enterprise License request - Data it reaches: the images and video you give it, plus the downloaded model files - Not for: commercial use under the open license alone, since the card tells commercial users to request an Enterprise License ## README ````markdown # YOLO26 Finds objects in images and video with the Ultralytics YOLO model family, which also covers segmentation, classification, depth, pose and tracking. For developers building computer vision apps. Made by Ultralytics on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: YOLO26 is an object detection model from Ultralytics, listed on Hugging Face for the ultralytics library. The company builds YOLO models for computer vision work, and the page says they cover detection, several kinds of segmentation, classification, depth, pose and tracking across video frames. Who it suits: It suits developers and researchers who build vision features and want a fast, widely used detector with documentation in several languages. ```table Industry | Who uses it | How they use it | Example Software development | App developers | Add object detection to an app that reads photos or video | Marking items found in an uploaded picture Computer vision research | Researchers | Use it as a baseline and compare other detectors against it | Testing a new dataset on the same task Video analysis | Engineers | Follow detected objects from one frame to the next | Tracking a moving object in a clip Robotics and devices | Hardware teams | Run a detector on camera input | Spotting objects in a live camera feed ``` You can skip it if you need a chat model or text generation, since this one works on images and video. Where to use it: - Python projects that use the ultralytics library - Windows, macOS or Linux with Python, to download the files - Vision pipelines that take photos or video frames as input ```table Use case | Who | What they get back Object detection | Developers | Boxes and labels for objects in an image Segmentation | Researchers | Outlines of objects at the pixel level Pose estimation | Engineers | Body or object keypoints for each detection Tracking | Video teams | Detected objects followed across frames ``` How to set it up: You need Python and the Hugging Face download tool. - Python on Windows, macOS or Linux - The huggingface_hub package - The ultralytics library for running the model 1. Install huggingface_hub. 2. Download the model files to your computer. 3. Follow the model card and the Ultralytics documentation to load them in the ultralytics library. ```sh pip install -U huggingface_hub hf download Ultralytics/YOLO26 ``` How to use it: The record gives the download step, and the model card points to the Ultralytics documentation for running the model. After the download you load the files with the ultralytics library and pick the task you need. ```sh hf download Ultralytics/YOLO26 ``` Example: You download the files, load the model in a Python project and pass it a photo. For a detection task you would expect boxes and class labels for the objects it finds, as the card describes for its detection work. For newcomers and experts: If you are new to computer vision, an object detector looks at a picture and draws boxes around the things it recognizes. A first small try is to download the files and run one photo through the library as the documentation shows. For people who know the field, the family spans detection, instance and semantic segmentation, classification, depth, pose and oriented boxes, plus tracking over video. The card gives no speed or accuracy figures here, so check the paper it links before you compare it with other detectors. ```flow Photo or video frame goes in The YOLO26 model finds objects Task output is boxes, masks or poses Tracking links objects across frames ``` Good to know: The card text visible here is mostly links and badges, so the practical run steps live in the Ultralytics documentation. - License: AGPL-3.0 - Cost to run: none named, though commercial use goes through a paid Enterprise License request - Data it reaches: the images and video you give it, plus the downloaded model files - Not for: commercial use under the open license alone, since the card tells commercial users to request an Enterprise License ## On Hugging Face, 13 November 2026 - Task: object detection - Library: ultralytics - Downloads in the last 30 days: 26,767 - Likes: 264 - License: agpl-3.0 - Languages: en, zh, ja, ru, de, fr, es, pt - Quantized versions: 60 - Fine-tunes: 140 - Tags: ultralytics, yolo, object-detection, instance-segmentation, image-classification, pose-estimation, tracking, semantic-segmentation, yolo26, model-index ## Links - Model: https://huggingface.co/Ultralytics/YOLO26 - Files: https://huggingface.co/Ultralytics/YOLO26/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Ultralytics/YOLO26 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Ultralytics/YOLO26 on Hugging Face](https://huggingface.co/Ultralytics/YOLO26) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | YOLO26 (this page) | 26,767 | 264 | — | agpl-3.0 | 2026-09-06 | | [Koharu Layout HighRes](https://mindket.com/p/73854038-7d0d-46c5-bbbc-ddd078e37797) | 125 | 5 | 46.6M | apache-2.0 | 2026-11-06 | | [DA3METRIC-LARGE](https://mindket.com/p/38985451-37e4-41ce-ab1f-15e932abb403) | 428,678 | 31 | 334M | apache-2.0 | 2025-11-13 | | [RuView by ruv](https://mindket.com/p/07d0e4a5-fd96-4a4d-8d23-bd9c1bb45bef) | 69 | 37 | — | mit | 2026-10-09 | | [d3-turbo](https://mindket.com/p/98d80d31-de45-4b9b-9ad7-ba14820f8211) | 4,186 | 133 | 26.1B | apache-2.0 | 2026-11-02 | | [d3-ultra](https://mindket.com/p/1e92f78a-2d4d-4abb-99b4-e7c0449b9fdc) | 2,398 | 61 | 26.1B | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · teleforce · 2026-06-23](https://news.ycombinator.com/item?id=48639434): Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models - [Bluesky · deployedbyai · 2026-10-27](https://bsky.app/profile/deployedbyai.bsky.social/post/3mtwnvfm3a25n): Ultralytics 8.4.190 adds Mask-IoU-aware label assignment for YOLO26-seg - [Hacker News · digitalPhonix · 2026-08-27](https://news.ycombinator.com/item?id=49470500): Simon Weckert creates Digital Camouflage shirt to avoid AI video surveillance: It already doesn't work. \> Weckert tested the shirt using Yolo, an open-source, real-time AI object detection system. Tested one of the images from the article here: https://playground.roboflow.com/models/ultralytics/yolo26 which detects a person just fine. (It's already a bad sign that it was… - [Bluesky · deployedbyai · 2026-10-26](https://bsky.app/profile/deployedbyai.bsky.social/post/3mtl5ivywy2dg): Ultralytics v8.4.187 YOLO26-pose OKS-only label assignment with pyramid-level gate - [Hacker News · teleforce · 2026-06-23](https://news.ycombinator.com/item?id=48640558): An Introduction to YOLO26: Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models: https://arxiv.org/abs/2606.03748 - [Bluesky · AI Adoption Agency · 2026-10-02](https://bsky.app/profile/aiadoptionagency.bsky.social/post/3mwvsqcqmkc2b): 👁️ Real time computer vision just got cheaper. YOLO26 delivers 6 vision tasks in one model family: ✅ Detection + segmentation + tracking ✅ 43% faster on CPU ✅ Export to TensorRT, CoreML, TFLite Full guide 👇 aiadoptionagency.com/yolo26-compu... - [Bluesky · What's New · 2026-09-08](https://bsky.app/profile/whatsnew.fyi/post/3muydxfo5cy2w): ultralytics v8.4.143 v8.4.143 introduces INT8 quantization-aware training for YOLO26, improves deployment and evaluation workflows, and delivers a broad documentation and integration refresh. 🚀 - [Bluesky · Hacker News Top Stories · 2026-06-23](https://bsky.app/profile/hackernewsbot.bsky.social/post/3mowpw6mys72w): Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models \| Discussion - [Bluesky · Hacker News Top 100 Bot (Unofficial) · 2026-06-23](https://bsky.app/profile/hn100.bsky.social/post/3mowhcupm4z2w): Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models Discussion - [Bluesky · arXiv cs.CV Computer Vision and Pattern Recognition · 2026-06-03](https://bsky.app/profile/cscv-bot.bsky.social/post/3mneivm4ucs2t): Glenn Jocher, Jing Qiu, Mengyu Liu, Shuai Lyu, Fatih Cagatay Akyon, Muhammet Esat Kalfaoglu: Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models https://arxiv.org/abs/2606.03748 https://arxiv.org/pdf/2606.03748 https://arxiv.org/html/2606.03748 - [Bluesky · Leo 🏳️🌈⚛️☭ · 2026-01-14](https://bsky.app/profile/leosmi.bsky.social/post/3mcf3cdqv6c2s): Release v8.4.0 - \`ultralytics 8.4.0\` YOLO26 Models Release (\#23176) · ultralytics/ultralytics github.com/ultralytics/... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-09-06 - First published: 2026-01-26 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Ultralytics/YOLO26 on Hugging Face](https://huggingface.co/Ultralytics/YOLO26) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6ef25cc2-4ee1-4222-97ad-21466d3c1efd) - [Its page on Mindket](https://mindket.com/p/6ef25cc2-4ee1-4222-97ad-21466d3c1efd)