title: SuperviseLab
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: static
pinned: true
license: mit
short_description: Training-ready video understanding datasets for model teams
SuperviseLab
Training-ready video understanding datasets for model teams
SuperviseLab helps video understanding and multimodal model teams turn raw video assets into structured, distilled, training-ready datasets.
We are not a generic annotation vendor. We focus on the part that matters for model builders:
- schema design for training and evaluation
- video understanding supervision
- multimodal data production
- human-in-the-loop QA workflows
- JSON / JSONL delivery for SFT, post-training, and benchmark pipelines
What we deliver
Video understanding distillation datasets
Structured supervision designed for teacher-student training, SFT-style post-training, and multimodal understanding tasks.
Evaluation and benchmark data
Holdout sets, benchmark tasks, rubric-based evaluation samples, and regression-style test data.
Preference and ranking data
Pairwise or rubric-based preference samples for post-training and quality optimization workflows.
Who this is for
SuperviseLab is built for:
- video understanding model teams
- multimodal foundation model teams
- post-training / alignment teams
- evaluation / benchmark teams
- startups that have raw video assets but need model-ready datasets faster
Start here
1. Overview
Understand what SuperviseLab is and how we work.
➡️ Open the SuperviseLab overview Space
2. Sample dataset
Inspect a public sample that demonstrates our delivery structure.
➡️ Open the video understanding distillation sample dataset
3. Schema explorer
Explore example schema structures for distillation, evaluation, and preference workflows.
➡️ Open the schema explorer Space
Why model teams talk to us
Because raw video assets are not training datasets.
A usable dataset for video understanding requires:
- task-specific schema
- structured supervision
- clip-level and sequence-level consistency
- OCR / ASR / speaker-aware alignment where needed
- QA and arbitration logic
- versioned delivery that can plug into a real training pipeline
That is the layer SuperviseLab focuses on.
Website
If you want to discuss a pilot, request a sample pack, or align on schema and acceptance criteria, the website is the best place to start.