Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 β object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]
We write your reusable computer vision tools. π
X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, point cloud and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
Turn any computer or edge device into a command center for your computer vision projects.
Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11, YOLOv12 for ROS 2
LibreYOLO is a MIT licensed open source computer vision library
Image augmentation for computer vision. AGPL-3.0-only or commercial licensing.
GTR: Gated Token Recurrence for Efficient Dense Prediction
pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.
Images to inference with no labeling (use foundation models to train supervised models).
Python Computer Vision & Video Analytics Framework With Batteries Included
Hierarchical perception library in Python for pose estimation, object detection, instance segmentation, keypoint estimation, face recognition, etc.
[CVPR 2025 Highlight] Official code and models for Encoder-only Mask Transformer (EoMT).
π A high performance real-time object detection solution using YOLO11 β‘οΈ powered by ONNX-Runtime
Ultralytics YOLO iOS app and Swift package for real-time Core ML inference across major computer vision tasks.
Official implementation of βA Turbo-Inference Strategy for Object Detection and Instance Segmentationβ (CVIU 2026). A training-free framework for iterative refinement between object detection and instance segmentation.
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.