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Google

Google: OWL-ViT

OWL-ViT Overview

OWL-ViT (Open-World Localization with Vision Transformers) is an open-vocabulary object detection model released in May 2022 by Google Research. It adapts a pretrained CLIP-style image-text model by removing the final pooling layer and attaching lightweight classification and box prediction heads to each Transformer output token, producing a detector capable of localizing arbitrary objects described by free-form text at inference time. Rather than being restricted to a fixed taxonomy such as the 80 categories in Microsoft COCO, OWL-ViT can detect object classes specified by a user's text query, including categories the model was never explicitly trained on.

OWL-ViT accepts an image and a list of text queries as input, and produces bounding boxes with class assignments drawn from the supplied queries. It also supports one-shot image-conditioned detection, where a cropped image region is used as the query instead of text, allowing the model to find visually similar instances within a target scene. The model is released in multiple Vision Transformer sizes (ViT-B/32, ViT-B/16, ViT-L/14) and CLIP-pretrained variants, distributed through the Google Research scenic repository and Hugging Face under the Apache 2.0 license. A successor model, OWLv2, was released in June 2023, introducing the OWL-ST self-training recipe that scales training to over one billion pseudo-annotated examples and substantially improves detection performance on rare and long-tail categories while preserving the open-vocabulary interface.

OWL-ViT Details & Performance

Details

Resources

GitHubPaper

Vision Tasks

Object Detection

Features

Zero-shot DetectionFoundation Vision

Usage

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OWL-ViT License

Apache 2.0

License terms and commercial-use guidance for OWL-ViT.

This model is released under the Apache License 2.0, a permissive open-source license that allows commercial use, modification, distribution, and patent use.

Read the full Apache 2.0 license ↗

Yes. Under the terms of the Apache 2.0 license, you can freely use this model for commercial purposes, including in proprietary products. You must retain the copyright notice and disclaimers when redistributing.

License information is provided as a guide and is not legal advice.