roboflow/supervision · error · ValueError

LabelMe shape of type {shape_type} is missing the required {

Error message

LabelMe shape of type {shape_type} is missing the required {missing} field.

What it means

Raised while parsing LabelMe JSON when a shape of a supported type (polygon/rectangle) is missing its 'label' or 'points' field (or points is null). Every LabelMe shape must carry both a class label and a point list; the message names which field is absent so you can fix the JSON directly.

Source

Thrown at src/supervision/dataset/formats/labelme.py:106

    Warns:
        UserWarning: When unsupported shape types are encountered and skipped.
    """
    xyxy_list: list[npt.NDArray[np.float32]] = []
    class_ids: list[int] = []
    polygons: list[npt.NDArray[np.float32]] = []
    skipped_types: set[str] = set()

    for shape in shapes:
        shape_type = shape.get("shape_type")
        if shape_type not in SUPPORTED_SHAPE_TYPES:
            skipped_types.add(str(shape_type))
            continue
        label = shape.get("label")
        points_raw = shape.get("points")
        if label is None or points_raw is None:
            missing = "label" if label is None else "points"
            raise ValueError(
                f"LabelMe shape of type {shape_type!r} is missing the "
                f"required {missing!r} field."
            )
        points = np.array(points_raw, dtype=np.float32)
        if points.ndim != 2 or points.shape[1] != 2:
            raise ValueError(
                f"LabelMe shape of type {shape_type!r} (label={label!r}) has "
                f"malformed points: expected an (N, 2) array, got shape "
                f"{points.shape}."
            )
        if shape_type == "rectangle":
            if len(points) < 2:
                raise ValueError(
                    f"LabelMe rectangle shape (label={label!r}) has "
                    f"{len(points)} point(s); expected at least 2."
                )
            xyxy = _rectangle_to_xyxy(points)
            polygon = _xyxy_to_polygon(xyxy)

View on GitHub (pinned to 7f254d9784)

Solutions

Open the failing .json and find the shape whose 'label' or 'points' key is absent (the error names the field and shape_type).
Add the missing field: a non-empty string label, and points as [[x, y], ...].
If the shape is junk, delete it from the shapes array.
Fix the upstream converter so it always writes both fields, then re-export.

Example fix

// before
{"shape_type": "polygon", "points": [[1, 2], [3, 4]]}
// after
{"shape_type": "polygon", "label": "cat", "points": [[1, 2], [3, 4]]}

Defensive patterns

Strategy: validation

Validate before calling

SUPPORTED = {'polygon', 'rectangle'}

def shapes_well_formed(shapes: list[dict]) -> bool:
    """Every supported LabelMe shape has label and points fields."""
    return all(s.get('shape_type') not in SUPPORTED
               or (s.get('label') is not None and s.get('points') is not None)
               for s in shapes)

Try / catch

try:
    dataset = sv.DetectionDataset.from_labelme(images_dir, ann_dir)
except ValueError as e:
    if 'missing the required' in str(e):
        raise SystemExit(f'Corrupt LabelMe JSON — add the named field: {e}') from e
    raise

Prevention

Use the official LabelMe app or battle-tested exporters to write JSON.
Lint annotation JSON in dataset CI before training runs.
Never hand-strip fields from LabelMe files.

When it happens

Trigger: DetectionDataset.from_labelme on a directory where some shape dict in a .json file lacks the 'label' or 'points' key — e.g. hand-written JSON, partial exports, or converter bugs.

Common situations: Annotations generated by scripts (not the LabelMe app) that omit fields; JSON edited by hand; older/alternative tools writing a schema without points for certain shapes; truncated files.

Related errors

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/0fe0ec12fda46972. Report an issue: GitHub.