Get startedModels · Availability limit
Understand VLM training availability
Multimodal dataset schemas do not imply an executable vision-language model trainer. General VLM fine-tuning is not an implemented training path in this release.
Source reviewed 2026-09-16. Availability depends on your installation, permissions, and compatible runtimes. A supported path is not a guarantee of model quality or production readiness.
Outcome
Choose a supported detection or local segmentation training path, or export compatible annotations for an external training system.
Start here
What you need to know first
Check the inputs below before starting. If you are new, begin with the first-project guide. A dataset holds media and labels; a model produces results; a deployment makes a selected model version callable. Creating one does not create the others.
Bring these inputs
- — Your intended task and annotation schema
- — An external training system if you need VLM fine-tuning
Expected output and limits
- — No in-app VLM training artifact is promised
- — Format-dependent data export for an external system
Core workflow
Check the task
The in-app trainer supports detection and local instance segmentation. A VQA or captioning schema is a data format, not proof of training support.
Prepare portable data
Review your images or sampled frames and annotations. Check that the export format preserves the fields your external trainer needs.
Choose a supported route
Use Training for supported detection or segmentation data. Do not launch a detector expecting it to learn image-question answering.
Verify external results
External models need a compatible runtime and artifact before Score Studio can run them. External training is not automatically registered or evaluated.
What this surface supports
Multimodal annotation schemas
Format-dependent annotation export
Detection training
Local segmentation training
Expert section
Contracts, signals, and failure modes
Use this section when you are defining acceptance criteria, automating the surface, or reviewing whether its output is safe to promote downstream.
Quality and operating signals
- — Task accuracy or answer quality
- — Grounding IoU/recall
- — Hallucination and abstention rate
- — Validation loss
Common failure modes
- — Catastrophic forgetting
- — Prompt-template leakage
- — Training on answers without visual evidence
- — Evaluating on near-duplicate images
Expert release checklist
□ Inputs and dependencies are pinned to immutable versions.
□ Acceptance metrics include critical classes and operating slices.
□ Failure, retry, cost, and rollback behavior are understood.
□ The resulting artifact has an owner and a downstream review path.
Engineering safeguards
- — No general VLM trainer is promised
- — Verify export fields before external use
- — Runtime compatibility is required