blip-2-vision-language

Process images for captioning, visual question answering, and image-text retrieval.

11.5k|842|Updated Nov 3, 2025

Search Tags:#multimodal #zero-shot #vision-language #image understanding #vqa #image captioning

Ready to run

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill blip-2-vision-language

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: blip-2-vision-language
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/18-multimodal/blip-2
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill blip-2-vision-language

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, Pillow, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables AI agents to understand and interpret images, bridging the gap between visual input and natural language processing for tasks like image captioning and visual question answering.

Core Features & Use Cases

Image Captioning: Generate descriptive text for any image.
Visual Question Answering (VQA): Answer specific questions about the content of an image.
Image-Text Retrieval: Find images that best match a given text description.
Use Case: Upload a photo of a landscape and ask, "What is the weather like in this picture?" or "Describe the main elements of this scene."

Quick Start

Use the blip-2-vision-language skill to generate a caption for the image located at /path/to/your/image.jpg.

Frequently Asked Questions about blip-2-vision-language

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate text descriptions from images using AI?▼

Visual question answering (VQA) answers specific questions about an image's content by combining visual processing with language models. This Skill uses BLIP-2 to analyze the image and generate a targeted textual response to your query.

What's the best way to perform zero-shot image understanding with Python?▼

Zero-shot image understanding is achieved by using pre-trained vision-language models like BLIP-2 to process images without additional training. This Skill leverages transformers and torch to perform multimodal tasks directly on your image data.

Do I need torch and transformers to run BLIP-2 for multimodal tasks?▼

Yes, you need torch, transformers, and Pillow installed to run BLIP-2 for multimodal tasks. These dependencies handle model inference and image manipulation required for generating textual outputs from visual inputs.

Can I use visual question answering to identify specific elements in a photo?▼

Yes, visual question answering can identify specific elements in a photo by answering targeted questions about the image content. The Skill processes the image and your question to produce a precise textual answer about the scene.

Does image-text retrieval work for finding photos that match a text description?▼

Image-text retrieval finds images that best match a given text description by comparing visual and textual features. This Skill supports this retrieval task alongside image captioning and VQA using the BLIP-2 model architecture.