Top Zero-Shot Object Detection Models

Zero-shot object detection models let you detect objects using an open vocabulary without training a custom model.

Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using Roboflow Inference.

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YOLO-World

YOLO-World is a zero-shot object detection model.

Object Detection

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Grounded SAM

GroundedSAM combines Grounding DINO with the Segment Anything Model to identify and segment objects in an image given text captions.

Instance Segmentation

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FastSAM

FastSAM is an image segmentation model trained using 2% of the data in the Segment Anything Model SA-1B dataset.

Instance Segmentation

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MetaCLIP

MetaCLIP is a zero-shot classification and embedding model developed by Meta AI.

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4M

The 4M model is a versatile multimodal Transformer model developed by EPFL and Apple, capable of handling a handful of vision and language tasks.

Object Detection

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OWLv2

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.

Object Detection

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OWL ViT

OWL-ViT is a transformer-based object detection model developed by Google Research.

Object Detection

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BLIPv2

BLIPv2 is a multimodal model developed by Salesforce Research.

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Florence 2

Florence-2 is a lightweight vision-language model open-sourced by Microsoft under the MIT license.

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Visual Question Answering

Image Similarity

Image Captioning

Zero-shot Detection

Real-Time Vision

Image Embedding

LLMS with Vision Capabilities

Multimodal Vision

Foundation Vision