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Link to this sectionReference for ultralytics/models/yolo/world/val.py#

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Summary

Link to this sectionClass ultralytics.models.yolo.world.val.WorldValidator#

WorldValidator()

Bases: DetectionValidator

A validator for YOLO-World models that sets dataset class names before validation.

Open-vocabulary YOLO-World models default to 80 COCO classes, so validating on a dataset with different classes (e.g. LVIS) fails or yields zero metrics. This validator generates text embeddings for the dataset's class names so standalone model.val() works. During training, classes are set via the on_pretrain_routine_end callback instead.

Methods

NameDescription
__call__Set dataset classes for standalone validation, then run validation.
Source code in ultralytics/models/yolo/world/val.py

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class WorldValidator(DetectionValidator):

Link to this sectionMethod ultralytics.models.yolo.world.val.WorldValidator.__call__#

def __call__(self, trainer = None, model = None)

Set dataset classes for standalone validation, then run validation.

Args

NameTypeDescriptionDefault
trainerNone
modelNone
Source code in ultralytics/models/yolo/world/val.py

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def __call__(self, trainer=None, model=None):
    """Set dataset classes for standalone validation, then run validation."""
    if trainer is None:  # standalone val; training sets classes via on_pretrain_routine_end callback
        self.device = select_device(self.args.device, verbose=False)
        if not isinstance(model, torch.nn.Module):
            from ultralytics.nn.tasks import load_checkpoint

            model = load_checkpoint(model or self.args.model, device=self.device)[0]
        model.eval().to(self.device)
        self.args.data = convert_ndjson_to_yolo_if_needed(self.args.data)  # match BaseValidator dataset handling
        names = [name.split("/", 1)[0] for name in check_det_dataset(self.args.data)["names"].values()]
        current = model.names.values() if isinstance(model.names, dict) else model.names  # names may be a list
        if list(current) != names:  # regenerate prompts only if class order differs from dataset
            state = (model.names, model.txt_feats, model.model[-1].nc)  # restore after to avoid leak to caller
            model.set_classes(names, cache_clip_model=False)
            model.names = dict(enumerate(names))  # set_classes updates embeddings/nc but not names
            try:
                return super().__call__(trainer, model)
            finally:
                model.names, model.txt_feats, model.model[-1].nc = state
    return super().__call__(trainer, model)



Contributors
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