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Top Zero-Shot Object Detection Models
Zero-shot object detection models let you detect objects using an open vocabulary without training a custom model.
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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
Deploy with Roboflow
Grounded SAM
GroundedSAM combines Grounding DINO with the Segment Anything Model to identify and segment objects in an image given text captions.
Zero Shot Segmentation
Deploy with Roboflow
FastSAM
FastSAM is an image segmentation model trained using 2% of the data in the Segment Anything Model SA-1B dataset.
Instance Segmentation
Deploy with Roboflow
MetaCLIP
MetaCLIP is a zero-shot classification and embedding model developed by Meta AI.
Classification
Deploy with Roboflow
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
Deploy with Roboflow
OWLv2
OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.
Object Detection
Deploy with Roboflow
OWL ViT
OWL-ViT is a transformer-based object detection model developed by Google Research.
Object Detection
Deploy with Roboflow
BLIPv2
BLIPv2 is a multimodal model developed by Salesforce Research.
Classification
Deploy with Roboflow
Florence 2
Florence-2 is a lightweight vision-language model open-sourced by Microsoft under the MIT license.
Open Vocabulary Object Detection
Deploy with Roboflow
Visual Question Answering
Image Tagging
Image Similarity
Image Captioning
Zero-shot Detection
Real-Time Vision
Image Embedding
LLMS with Vision Capabilities
Multimodal Vision
Foundation Vision