Image annotation review

Roboflow holds labeled image datasets used to train vision models. A Beam agent reviews incoming annotations against your labeling guide, reading each image's classes and boxes and checking for the mistakes that hurt models, like missing labels or overlapping boxes. Clean images are marked ready for training. Ones that break your rules are sent back with a note on what to fix. Edge cases, such as an object your guide never anticipated or a class boundary that is genuinely unclear, are queued for a human labeler, since a wrong call there teaches the model the wrong thing.

Model inference calls

A trained Roboflow model can score new images through its hosted inference. A Beam agent sends an incoming image to the deployed model where your connector and keys allow, reads back the predictions and confidence scores, and applies your thresholds, such as auto-accepting only high-confidence detections. Confident results feed the next step, like a tag or a count, and are written to your record. Low-confidence or empty predictions are not trusted blindly; they are routed to a person to confirm, so uncertain machine guesses never pass silently into a decision that matters.

Dataset versioning

Roboflow tracks dataset versions as images and labels change. A Beam agent watches for new uploads, reads the batch, and applies your intake rules, checking format, resolution, and class names before folding images into the working set. It records what was added and notifies the owner when a version is ready. Uploads that fail a check, duplicate existing images, or arrive with unexpected classes are held out and reported rather than merged. This keeps each training version clean and documented, so a person can trust what changed between one model run and the next.