VisionML
Model training that ships a manifest saying exactly what it made.
What it is
A standalone model production service with a command line, visionml. It loads an image-folder
dataset, trains, evaluates, exports to ONNX, and registers the result with a manifest.json.
The manifest is the product. Schema v2 records model identity and task type, the artifact’s sha256, the preprocessing recipe the consumer must reproduce, output tensor bindings, validated decision thresholds, provenance (dataset hash, seeds, framework versions), parity evidence against the training framework, the pinned runtime contract, and an acceptance gate with its configured criteria and measured values.
Who it is for
Whoever trains the model, and whoever later has to say what that model was. VisionML is app-neutral by design: VisionAI is a consumer, not the owner, and no consumer-specific logic lives here.
How it is governed
Registration fails closed. If a configured acceptance criterion fails, or the metric it needs is missing, the model is not registered. Parity evidence must pass unless it is explicitly waived, and a waiver is recorded as such. The preprocessing recipe is part of the contract because train-and-serve skew is otherwise invisible: interpolation is linear only, and any other value fails closed.
What it does not do
It does not run inference in production; consumers do that. It does not decide that a model is approved for a line or a bench — that is a change-controlled decision made by the people who own the process. Controlled lifecycle states and signed artifact distribution are recorded as roadmap work, not as shipped behaviour.
Status
In development. Real ONNX export is scheduled work; today the exporter writes a placeholder graph, and the repository says so.
Related
VisionAI consumes the manifest through its inference backend. Digital Labs consumes the same shape of package on device, verifying the digest before it will run anything.