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Explore the Roboflow Universe. The world's largest collection of open source computer vision datasets and APIs. 350 million+ images. 500,000+ datasets. 100,000+ fine-tuned models.
Deploy with 50,000+ pre-trained models Test prototype models in the browser and use available APIs in production. Find models. Try a model in the browser using image or video data. Use infinitely scalable cloud APIs with SDKs and code snippets directly in your application.
Browse thousands of computer vision image and video datasets and pre-trained models.
Aug 11, 2021 · Roboflow Universe is designed to bridge the gap between the worlds of research and engineering. Extensive knowledge of machine learning is no longer required to use a model in your app. Every dataset released on Roboflow Universe can be trained using Roboflow Train.
1 - 30 of 100k+. Open source computer vision datasets and pre-trained models. Showing all projects by subject, page 1.
Download or clone images from Roboflow Universe for use in your projects. Roboflow Universe hosts open-source datasets and pre-trained models for computer vision. Over 200,000 Datasets and 50,000 pre-trained models are available. There are two methods to adding Universe images to your dataset.
Jun 28, 2022 · 90,000 Datasets and 7,000 Pre-trained Models Available. Roboflow Universe launched in August 2021 with 50 open source datasets and opened our computer vision infrastructure products for free with a Public plan.
Mar 27, 2023 · Roboflow Universe is the largest open-source repository of computer vision datasets and pre-trained models, featuring over 150 million images divided into 200,000 datasets, and 50,000+ models.
Our mission is to democratize computer vision. Discover 200k+ datasets, reusable computer vision tools, automated data labeling, model training pipelines, a production-ready inference server, and more.
Join over 500,000 developers building with Roboflow. Try it for Free Talk to Sales. Everything you need to build and deploy computer vision models, from automated annotation tools to robust, device-agnostic deployment solutions.
