VideoLLaMA 2

Preview：

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Introduce：

VideoLLaMA 2 is a large-scale language model optimized for video understanding tasks that improves the parsing and understanding of video content through advanced spatial-temporal modeling and audio understanding capabilities. The model has shown excellent performance on tasks such as multi-choice video question answering and video title generation.

Stakeholders:
VideoLLaMA 2 is suitable for researchers and developers who need efficient video content analysis and understanding, especially in video understanding tasks such as video question-and-answer, video caption generation, etc.
Usage Scenario Examples:

The researchers used VideoLLaMA 2 for the development of an automated question-and-answer system for video content.
Content creators use this model to automatically generate video captions to improve work efficiency.
Businesses are using VideoLLaMA 2 in video surveillance analytics to improve incident detection and response speed.

The features of the tool:

Supports seamless loading and reasoning of base models.
Provide online demo for users to quickly experience model functions.
Ability to generate video Q&A and video captions.
Code that provides training, evaluation, and modeling services.
Supports training and evaluation of custom datasets.
Detailed installation and usage guidelines are provided.

Steps for Use:

First, make sure you have the necessary base dependencies installed, such as Python, Pytorch, and CUDA.
Get the codebase for VideoLLaMA 2 via the GitHub page and follow the guide to install the required Python packages.
Prepare the required checklioints for the model and follow the documentation to start the model service.
Use the provided scripts and command-line tools to train, evaluate, or reason the model.
Adjust the model parameters as required to optimize the model performance.
Run online demos or local model services to experience video understanding and generation of models.

Tool’s Tabs: Video understanding, spatial-time modeling