Open-Weight Models Under GNU Affero General Public License 3.0

Model

yolo-world-mirror

Dowon Hwang

https://docs.ultralytics.com/models/yolo-world/#available-models-supported-tasks-and-operating-modes model weights for ultralytics yolo models

Model · Image segmentation

DelineateAnything

Mykola Lavreniuk

Delineate Anything v2 extends Delineate Anything into a globally representative, resolution-agnostic foundation model that scales agricultural field boundary detection to a planetary level from any imagery source. Trained on FBIS-73M, a massive 73-million-instance dataset spanning 61 countries with diverse imagery sources ranging from 0.25m to 10m resolution, built through a resolution-specific curation pipeline that solves the parcel-versus-field mismatch, Delineate Anything v2 sets a new state-of-the-art in global zero-shot delineation. It delivers a +103.3% relative gain in [email protected] over Delineate Anything while maintaining extreme efficiency, mapping all of Ukraine (603,000 km²) in 5.4…

Model · Object detection

Anzhcs_YOLOs

Anzhc

YOLOs in this repo are trained with datasets that i have annotated myself, or with the help of my friends(They will be appropriately mentioned in those cases). YOLOs on open datasets will have their own pages. Ultralytics 8.3.217 updates mask handling, which breaks function in main Adetailer repo. Install Ultralytics==8.3.216 or lower. Alternatively - use forks that fix this. - Fixed in main repo. I've added some features to make Adetailer more usable and less manual - https://github.com/Anzhc/aadetailer-reforge Im open to commissions, hit me up in Discord - anzhc P.S. All model names in tables have download links attached:3 Series of models aiming at detecting and segmenting face…

Model · Object detection

yolov11-license-plate-detection

Nuttapong Chimwai

This is a fine-tuned version of YOLOv11 (n, s, m, l, x) specialized for License Plate Detection, using a public dataset from Roboflow Universe: The upstream Roboflow dataset (license-plate-recognition-rxg4e) contains train/test contamination — the same source images appear in both the training and test splits with only minor manual augmentation applied (see Discussion #2 for concrete examples). As a result: - The reported metrics below are likely overestimated, because the test set is not a true held-out evaluation. - Real-world generalization performance is expected to be lower than the numbers in the table. - Treat all evaluation figures with caution and validate the model on your own…

Model · Object detection

YOLO-Face-Person-Detector

Irina Tolstykh

This model is a fine-tuned version of YOLOv8x specialized in detecting two specific classes: Face and Person. It has been trained on a large-scale proprietary dataset consisting of approximately 150,000 images. The high capacity of the YOLOv8x architecture combined with a diverse proprietary dataset ensures high accuracy and robustness in various scenarios. You can load the model using the Hugging Face transformers library by enabling custom code execution. If you prefer the standard Ultralytics API, you can download the weights from the Hub and load them directly. This method automatically handles model downloading for ultralytics YOLO model. This model is based on the Ultralytics YOLOv8…

Model · Object detection

YOLO11

Ultralytics

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums! Request an Enterprise License for commercial use at Ultralytics Licensing. See below for quickstart installation and usage examples. For comprehensive guidance on…

Model · Object detection

YOLO26

Ultralytics

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, instance segmentation, semantic segmentation, image classification, depth estimation, pose estimation and oriented object detection tasks, and can track detected objects across video frames. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Model · Object detection

yolov8_animeface

Fuyumi

Anime Face Detection using YOLOv8 Dataset was made created with 10 000 images manually annotated from safebooru. Split was train 70, val 20, test 10. This model is based on yolov8x6. It has been trained on the said dataset for 300 epoch at 1280px1280px. It took ~110 hours to train on a RTX A4000. On my dataset, the model performs particularly well with the default parameters. While it doesn't provide a huge mAP50-95, its predictions are always correct on the files I've tested. Confidence could be higher, but the model is very precise. Confusion matrix of the model, generated on the test set PR curve of the model, generated on the test set Other metrics are available in the./demo/val…

Model · Object detection

YOLOv8

Ultralytics

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums! Request an Enterprise License for commercial use at Ultralytics Licensing. See below for quickstart installation and usage examples. For comprehensive guidance on…

Model · Object detection

Ultralytics

Midnight1111

ComfyUI Ultralytics Integration – Midnight1111 Model Collection For ComfyUI, here’s how to load these models: 1. Locate your ComfyUI folder (e.g. ~/ComfyUI/ or C:\ComfyUI\). 2. Create directories: 3. Place your.pt files: • Segmentation → models/ultralytics/segm/ • Detection (bbox) → models/ultralytics/bbox/ 5. In the UI: Add Node → Model → Ultralytics → choose segm/… or bbox/…. Connect an Image Loader → Ultralytics node → Previewer Unsafe files Since getattr is classified as a dangerous pickle function, any segmentation model that uses it is classified as unsafe. All models were created and saved using the official Ultralytics library, so it’s safe to use files downloaded from a trusted…

Official PyTorch implementation of YOLOE. Comparison of performance, training cost, and inference efficiency between YOLOE (Ours) and YOLO-Worldv2 in terms of open text prompts. Ao Wang, Lihao Liu, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding\ We introduce YOLOE(ye), a highly efficient, unified, and open object detection and segmentation model, like human eye, under different prompt mechanisms, like texts, visual inputs, and prompt-free paradigm. Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.…

Model · Object detection

YOLOV8s-Barcode-Detection

Andrea Pietrobon

This repository hosts a finetuned Ultralytics YOLOv8s model specifically designed for barcode and QR code classification and detection. Building upon the robust and efficient architecture of YOLOv8, this model has been fine-tuned on a comprehensive dataset of over 5,000 images, encompassing various barcode types (EAN13, Code128, etc.) and QR codes. YOLOv8 is renowned for its speed, accuracy, and ease of use, making it an excellent foundation for specialized object detection tasks like barcode and QR code recognition. This finetuned model aims to provide a reliable and performant solution for applications requiring automated barcode and QR code scanning and identification. We hope this model…

Model · Summarization

distilbart-news-summarizer

Sachin Rao

Imagine you have a very long news article, and you want someone to read it and tell you the key points in just a few sentences. That's exactly what this model does! This model takes a long news article and turns it into a short, easy-to-read summary. - It writes back a 3-4 sentence summary that captures all the important information The special thing about this model is that it's: 1. Very accurate - It understands news writing style very well 2. Very fast - It works quickly even on regular computers (not just expensive AI servers) 3. Specialized in news - It was trained specifically on news articles, so it understands how journalists write 4. Good with financial news - It knows market…