For AI agents: a documentation index is available at the root level at /llms.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
Skip to navigation0.14.0
New
- Dataset report checks the labels on the images. Dataset insights shows the AI model a few flagged frames and a few random ones with the labels and a detector’s predictions side by side, so the report can tell bad labels from a weak model. The report opens with a verdict and the steps to take first, and a problem that several checks found gets one section.
- Evaluations point at label problems. Evaluate object detection and Evaluate prelabeling efficiency report confident disagreements with the labels, boxes that sit off the objects in one direction, labeled boxes no prediction comes near, and a detector that does not know the domain yet. Evaluate object detection can now feed Findings from other nodes in Dataset insights.
- Grounding DINO detects the dataset’s classes. In Predict Grounding DINO, What to detect has a Dataset classes option: the node asks for every class of the ontology the source dataset is pinned to, so a flow needs nothing but the dataset.
- Workspace storage. A workspace can connect storage providers and buckets of its own under Settings > Storage. Extra storage is a plan feature.
Improvements
- Flow groups: drop nodes and sticky notes on a group’s frame, or add them from its menu, to make them members. Frames wrap their members’ edges, and edges end at the centre of a handle.
- The setup wizard of a flow template opens on its overview, can name each dataset on its own, and a template keeps a Dataset node’s own name.
- Node logs show the node’s own output and plain status lines, such as waiting for a machine, pulling the image, out of memory or evicted.
- Nodes with an image preview have one width, so datasets side by side are the same size.
- Move carries the annotations along with the images.
- Embedded public flows let the page scroll with the mouse wheel and on touch.
Fixes
- Nodes that use the instance’s AI model, such as Dataset insights, work with Claude Sonnet 5.5, Claude Opus 5.5 and Claude Fable 5.1.
- Grounding DINO predictions with classes picked from a shared ontology, such as the COCO classes, no longer fail at the end of the run.
- YOLO import reads class names written as a dictionary in
data.yaml.
- The header of the labeling tasks list fits its width.
