VideoLLaMA3

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VideoLLaMA3 is an Open Source & Models tool that provides frontier multimodal foundation models for image and video understanding. It offers models for general image and video understanding, multi-image comparison, and fine-grained recognition.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is VideoLLaMA3?

VideoLLaMA3 is an open-source project offering a series of multimodal foundation models designed for advanced image and video understanding. It provides models like VideoLLaMA3-7B and VideoLLaMA3-2B, which are capable of tasks ranging from general image and video comprehension to more specialized applications such as multi-image comparison, visual referring, and grounding. The project includes detailed instructions for inference, training, and evaluation, making it suitable for researchers and developers. It supports various benchmarks for performance assessment and offers a flexible framework for preparing custom training data. The models are available on Hugging Face, facilitating easy access and integration into AI development workflows.

Best used for

Ideal for AI researchers and developers who need to implement and evaluate advanced multimodal AI models for image and video understanding. Especially valuable for those working on projects requiring detailed visual analysis, model benchmarking, or custom model training within an open-source framework.

Common actions

understand images

understand videos

train AI models

evaluate AI models

low-code/no-codeautomated workflowcollaborationdeepfakeopen-sourceworkflowsface swapping"AI Agents"github copilot

Capabilities

Key features

Image understanding
Video understanding
Multi-image comparison
Visual referring
Model training
Evaluation benchmarks

Target Audience

professordeveloperresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary capabilities of VideoLLaMA3 models?

VideoLLaMA3 models are designed for frontier image and video understanding. They can perform general image and video comprehension, multi-image comparison, fine-grained image recognition, visual referring, and temporal grounding in videos, making them versatile for various visual AI tasks.

How can I get started with VideoLLaMA3 for inference?

To start with inference, you need Python >= 3.10, PyTorch >= 2.4.0, and CUDA >= 11.8. Install the required packages including `torch`, `torchvision`, `flash-attn`, `transformers`, `accelerate`, and video processing dependencies like `decord` and `ffmpeg-python`. Code examples are provided for quick setup.

Can I train VideoLLaMA3 models on my own datasets?

Yes, VideoLLaMA3 supports training on custom datasets. You need to organize your image and video data and create annotation files in a specific JSONL format. The project provides scripts and templates for preparing data, converting checkpoints, and initiating the training process.

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Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra

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