#vision

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.

With SAM 3 you can use text and visual prompts to precisely identify, segment, and follow any object in images or videos—coming soon to Instagram Edits and Vibes on the Meta AI app.

CVAT Community is the free, self-hosted open-source edition of CVAT — one of the most widely used data annotation platforms for building high-quality visual datasets for computer vision and visual AI. Since 2018, CVAT has become one of the best-known data annotation tools in computer vision, with a large open-source community, millions of Docker pulls, and broad adoption across research and production AI teams.

Segment Anything Model 2 (SAM 2) is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect our SA-V dataset, the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.

Turn raw visual data into model-ready datasets with CVAT annotation platform. Run it on your own infrastructure, CVAT cloud, or let our labeling team do the work.

You program the OpenMV Cam in Python. We make it easy to run machine vision algorithms and AI models on what the OpenMV Cam sees and then actuate hardware in the real world. Sense, plan, and act all in one Python script.

What makes microcontrollers unique is their low power consumption, low heat generation, small size, and ability to draw microwatts of power in deep sleep. This enables you to build tiny devices that can survive for years on batteries.

Beyond putting all these features into such a small footprint, we believe in giving you the tools to easily integrate the OpenMV Cam into any system. Each board exposes plenty of GPIO pins that provide SPI, I2C, I3C, UART, CAN, PWM, and ADC functionality.

For professionals, our schematics are available online so you can fully understand every OpenMV Cam and its accessories. You can modify and compile our firmware from GitHub, and SWD and JTAG are exposed for you to single step and debug your changes.

Vision language models are broadly defined as multimodal models that can learn from images and text. They are a type of generative models that take image and text inputs, and generate text outputs. Large vision language models have good zero-shot capabilities, generalize well, and can work with many types of images, including documents, web pages, and more. The use cases include chatting about images, image recognition via instructions, visual question answering, document understanding, image captioning, and others. Some vision language models can also capture spatial properties in an image. These models can output bounding boxes or segmentation masks when prompted to detect or segment a particular subject, or they can localize different entities or answer questions about their relative or absolute positions.

Docs for the OAK-D Lite.

Luxonis OAK-D Lite datasheet.