ultralytics

Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

Official Ultralytics YOLO Flutter plugin for real-time inference on Android and iOS across major vision tasks.

Easy-to-use finetuned YOLOv8 models.

Dual-modal YOLO Object Detection Framework

Reliable face detection, segmentation and enhancement for ComfyUI with custom-trained models that work across anime, realism, extreme poses and occlusion.

Huggingface utilities for Ultralytics/YOLOv8

Train YOLO + VLM with one command. Auto-generate vision-language training data from YOLO labels - no extra labeling needed.

High-accuracy Russian ANPR system built with YOLOv8 for detection and an optimized, custom-trained PyTorch CRNN for OCR.

🚀 Pre-trained YOLO for Document Layout Analysis. Instantly find text 📄, tables 📊, and figures 🖼️. Fast, accurate, and ready to use! ✨

A fine tuned yolov111 model to detect fire

YOLOv26 and RF-DETR object detection for civilians, rescuers, and animals in disaster scenarios. Real-time support for emergency response in floods and structural collapses.

An end-to-end tennis video analysis system leveraging Ultralytics YOLOv8 for player and ball detection, multi-object tracking across frames, and a custom CNN for court keypoint extraction, providing data-driven insights into player movement, ball trajectories, and match dynamics using advanced computer vision and deep learning techniques.