Top Vision-Language Models

Vision-language models have been pre-trained on image-text pairs to enable zero-shot predictions for visual recognition tasks. Visual language models can be used for multimodal tasks like visual question answering, visual captioning, and image tagging.

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

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CogVLM

CogVLM shows strong performance in Visual Question Answering (VQA) and other vision tasks.

Vision-Language

Deploy with Roboflow

QwenVL

Qwen-VL is an LMM developed by Alibaba Cloud. Qwen-VL accepts images, text, and bounding boxes as inputs. The model can output text and bounding boxes. Qwen-VL naturally supports English, Chinese, and multilingual conversation.

Vision-Language

Deploy with Roboflow

BakLLaVA

BakLLaVA is an LMM developed by LAION, Ontocord, and Skunkworks AI. BakLLaVA uses a Mistral 7B base augmented with the LLaVA 1.5 architecture.

Vision-Language

Deploy with Roboflow

Anthropic Claude 3

Vision-Language

Deploy with Roboflow

Google Gemini

Gemini is a family of Large Multimodal Models (LMMs) developed by Google Deepmind focused specifically on multimodality.

Vision-Language

Deploy with Roboflow

Visual Question Answering

Image Similarity

Image Captioning

Zero-shot Detection

Real-Time Vision

Image Embedding

LLMS with Vision Capabilities

Multimodal Vision

Foundation Vision

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