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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

Past 30 Days

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Performance

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OWL-ViT License

Apache 2.0

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

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