Video-LLaMA

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Video-LLaMA is an AI Agents & Automation tool that empowers large language models with video and audio understanding capabilities. It is an open-source project for research and development in multimodal learning.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Video-LLaMA?

Video-LLaMA is an instruction-tuned audio-visual language model designed for comprehensive video understanding. Built upon BLIP-2 and MiniGPT-4, it integrates both Vision-Language (VL) and Audio-Language (AL) branches. The VL branch, utilizing a ViT-G/14 visual encoder and BLIP-2 Q-Former, processes video representations and is trained on datasets like Webvid-2M and LLaVA image captions. The AL branch, powered by ImageBind-Huge, handles audio representations. The tool supports pre-training and fine-tuning stages, allowing for customization and enhanced instruction-following capabilities using datasets from MiniGPT-4, LLaVA, and VideoChat. It is an open-source project, making it accessible for AI researchers and developers to explore and build upon.

Best used for

Ideal for developers and data scientists who need to conduct advanced research in multimodal learning, develop sophisticated video understanding models, and integrate audio-visual capabilities into large language models. Especially valuable for those working on open-source AI projects and requiring detailed control over model training and architecture.

Common actions

understand video content

process audio-visual data

develop multimodal AI

fine-tune language models

collaborationlow-code/no-codeworkflowsopen-sourcedeepfakeautomated workflowgithub copilot"AI Agents"face swapping

Capabilities

Key features

Audio-Visual language model
Vision-Language branch
Audio-Language branch
Instruction-tuned
Pre-training capabilities
Fine-tuning capabilities
Supports LLaMA-2-Chat

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the core components of Video-LLaMA?

Video-LLaMA is composed of two main branches: the Vision-Language (VL) Branch, which processes video representations using a visual encoder and Q-Former, and the Audio-Language (AL) Branch, which handles audio representations via an audio encoder and Q-Former.

What language models does Video-LLaMA support?

Video-LLaMA primarily supports LLaMA-2-7B/13B-Chat as its language decoder. Earlier versions also supported Vicuna as a language decoder, with instructions available for its use.

What kind of data is used for training Video-LLaMA?

Video-LLaMA is pre-trained on datasets like Webvid-2.5M (video-caption pairs) and LLaVA-CC3M (image-caption pairs). For instruction fine-tuning, it uses data from MiniGPT-4, LLaVA, and VideoChat.

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