Annotation Export

WeightsLab can export bounding-box/segmentation annotations to a relabeling-tool format, so a dataset (or a slice of one) can be handed off for an outsourced relabeling pass. Three ways to trigger it, all backed by the same code path:

• Weights Studio UI — an “Export” button next to Save/Grid settings, with a format picker (CVAT / Label Studio / V7). Triggers a browser download.

• CLI — weightslab export connects over gRPC to a running experiment, same as weightslab cli.

• Python — wl.export_annotations(), called in-process (no gRPC round-trip needed since it already runs alongside the registered dataframe).

Supported formats

Format	Output shape	Schema reference
cvat	A single CVAT XML 1.1 file (one <image> element per sample, with <box>/<polygon> children).	CVAT XML format
label_studio	A single JSON file — a list of “tasks”, each with a result list of rectanglelabels/polygonlabels entries. Coordinates are percentages (0-100) of the image’s width/height, per Label Studio’s convention.	Label Studio export format
v7	A zip of one Darwin JSON 2.0 file per image (V7 matches annotations to images by filename on import).	Darwin JSON reference

Bounding boxes are exported for every format. Segmentation masks are converted to polygons via OpenCV contour extraction — this needs the optional export extra:

pip install weightslab[export]

Bounding-box-only export needs no extra dependency; if OpenCV isn’t installed, segmentation samples still export their boxes and a warning is logged once, rather than failing the whole export.

Usage

Python

import weightslab as wl

wl.export_annotations("cvat")                              # everything, under root_log_dir
wl.export_annotations("label_studio", "val.json", origin="val_loader")
wl.export_annotations("v7", "out/", class_names=["bg", "cat", "dog"])
wl.export_annotations("cvat", tags=["ToReview"])           # only samples tagged ToReview

See User Functions Reference for the full wl.export_annotations() reference.

CLI

weightslab export --format cvat                      # everything, CVAT XML, into "."
weightslab export -f v7 out/ --origin val_loader      # V7/Darwin, val split only
weightslab export -f cvat --tag ToReview              # only samples tagged ToReview

Connects over gRPC to a running experiment (127.0.0.1:50051 by default), same as weightslab cli. See User Commands Reference for every flag.

Weights Studio UI

The download-arrow icon button sits in the Details panel’s header actions, between the manual-save and grid-settings buttons. Clicking it opens a small floating menu next to the button:

• A tag filter section — one checkbox per existing tag:<name> column (boolean or categorical), only shown if any tags exist. Leave every box unchecked to export the whole dataset; check one or more to restrict to samples carrying any of them.

• Three format buttons — “Export to CVAT (XML)”, “Export to Label Studio (JSON)”, “Export to V7 / Darwin (zip)”.

Clicking a format button fires the ExportAnnotations gRPC call immediately (with the checked tags, or none) — there is no format preview step. A toast shows “Exporting annotations…”, then either a success message with the image count and a browser download of the file, or an error message if the call fails. The UI always exports ground-truth targets; it does not currently expose the use_predictions/--predictions toggle that the Python and CLI paths have.

Note

The export button is disabled in sandbox mode, with a tooltip explaining why — sandbox sessions can’t download data out of the demo.

In-app chat agent

Because the chat agent (see Experiment Agent Assistant) has general tool access to the live experiment process, you can also just ask for this in plain language – e.g. “export the samples tagged ToReview to CVAT format for relabeling” – and it calls wl.export_annotations() with the matching tags= argument itself. No special wiring is needed beyond the API existing.

Filtering by tag

All three entry points accept a tag filter (tags= in Python, --tag on the CLI, repeatable; the tag picker in the UI) that restricts the export to samples carrying any of the given tags – boolean tags set via wl.tag_samples() or categorical values set via wl.set_categorical_tag() both work, since they share the same tag:<name> column. Omit it to export every sample. This is the mechanism for a “send only what needs another look” relabeling handoff, e.g. tagging uncertain samples as ToReview during data exploration and exporting just that subset.

How annotations are resolved

Every export path collects annotations from the same registered dataframe that backs the rest of WeightsLab (get_dataframe()), grouping the (sample_id, annotation_id) multi-index rows by sample:

• Boxes — read from the target (or prediction, with use_predictions=True) column when it holds coordinate-shaped data ((x1, y1, x2, y2[, conf][, cls])), whether that’s a single box per sample or several boxes exploded across annotation rows.

• Masks -> polygons — read from the same column when it holds a dense (H, W) array (pixel value = class id); one polygon per connected region per class id.

Two real gaps in the current data model drive the “best effort” behavior below — call these out explicitly if an export looks wrong:

• No dedicated class-id -> name registry. Labels are resolved, in order: an explicit class_names argument; else a class_names attribute on the dataset object backing the relevant split; else "class_<id>".

• No per-sample stored image path or dimensions. A real image path is best-effort resolved from a few common dataset attribute names (image_paths, img_files, images, imgs, files, samples); dimensions come from that file (via Pillow) or, for segmentation samples, directly from the mask’s own shape. When no path resolves, the exported filename is synthetic (sample_<id>.jpg) — no image file is copied or embedded, so you must ensure the filenames you upload to CVAT/Label Studio/V7 match the ones in the export.