tao-generate-video-reasoning-annotations

You can generate Chain-of-Thought training data from raw videos by using an automated pipeline that creates multi-level captions, structured descriptions, and QA pairs with reasoning traces. This Skill processes video datasets to output CoT Q&A training data.

Automating video annotation for domain-specific CoT datasets is achieved by processing raw videos through a pipeline that supports built-in or custom prompts. It generates structured descriptions and QA pairs tailored to your specific domain requirements.

Yes, generating video reasoning annotations requires Docker with nvidia-container-toolkit installed. The pipeline also requires VLM and LLM endpoints, alongside Python packages for video processing and model inference.

Yes, you can use OpenAI and Gemini endpoints to create multi-level captions and QA pairs from videos. The pipeline depends on these VLM and LLM endpoints to extract reasoning traces and structured descriptions from the video content.

To build a CoT training dataset from traffic camera videos, run the auto_label generate command with a spec file and output directory. This processes the raw security camera videos into CoT Q&A training data with reasoning traces.

When converting raw videos into CoT training data, limitations include dependency on external VLM and LLM endpoints for inference, and the necessity of a Docker environment with nvidia-container-toolkit for processing video datasets.