Model Evaluations

A model evaluation captures how a model performs on a Version's test split - per-class metrics, confidence-threshold curves, image-embedding clustering, per-image predictions, and improvement recommendations. For object detection and instance segmentation the headline metric is mAP; for semantic segmentation it is mIoU. Evaluations are produced automatically when a training completes and can be re-triggered manually from the app.

The Model Evaluations API lets you read everything the app's evaluation page shows. Each panel in the UI maps to a dedicated endpoint:

• List model evaluations in a workspace

• Get one evaluation's metadata and headline metrics

• Get full per-split metric detail (mAP or mIoU)

• Get the confidence-threshold sweep and F1-optimal thresholds

• Get per-class performance for one split

• Get the confusion matrix

• Get the image-embedding clustering (vector analysis)

• Get per-image predictions

• Get model improvement recommendations

Authentication

All endpoints require an API key with the model-eval:read scope. Pass it as a query parameter or as a Bearer token in the Authorization header.

Common errors

Status	Error code	When
401	unauthenticated	API key missing or invalid
404	model_eval_not_found	Evaluation does not exist or belongs to a different workspace
409	model_eval_not_done	Evaluation has not completed; the panel data is not yet available
400	invalid_confidence	confidence query parameter is not an integer in [0, 100]
400	invalid_split	split query parameter is not one of the allowed values for the endpoint