open-mmlab/mmdetection · info

palette does not exist, random is used by default. You can a

Error message

palette does not exist, random is used by default. You can also set the palette to customize.

What it means

Warning from _load_weights_to_model: after checking args.palette, config dataset metainfo, and checkpoint, no palette was found anywhere, so 'random' palette is assigned for visualization colors.

Source

Thrown at mmdet/apis/det_inferencer.py:154

                          'result is calculated by the randomly initialized '
                          'model!')
            warnings.warn('weights is None, use COCO classes by default.')
            model.dataset_meta = {'classes': get_classes('coco')}

        # Priority:  args.palette -> config -> checkpoint
        if self.palette != 'none':
            model.dataset_meta['palette'] = self.palette
        else:
            test_dataset_cfg = copy.deepcopy(cfg.test_dataloader.dataset)
            # lazy init. We only need the metainfo.
            test_dataset_cfg['lazy_init'] = True
            metainfo = DATASETS.build(test_dataset_cfg).metainfo
            cfg_palette = metainfo.get('palette', None)
            if cfg_palette is not None:
                model.dataset_meta['palette'] = cfg_palette
            else:
                if 'palette' not in model.dataset_meta:
                    warnings.warn(
                        'palette does not exist, random is used by default. '
                        'You can also set the palette to customize.')
                    model.dataset_meta['palette'] = 'random'

    def _init_pipeline(self, cfg: ConfigType) -> Compose:
        """Initialize the test pipeline."""
        pipeline_cfg = cfg.test_dataloader.dataset.pipeline

        # For inference, the key of ``img_id`` is not used.
        if 'meta_keys' in pipeline_cfg[-1]:
            pipeline_cfg[-1]['meta_keys'] = tuple(
                meta_key for meta_key in pipeline_cfg[-1]['meta_keys']
                if meta_key != 'img_id')

        load_img_idx = self._get_transform_idx(
            pipeline_cfg, ('LoadImageFromFile', LoadImageFromFile))
        if load_img_idx == -1:
            raise ValueError(

View on GitHub (pinned to cfd5d3a985)

Solutions

1. Pass palette explicitly: DetInferencer(..., palette='coco')
2. Add 'palette' to your dataset's METAINFO in the config
3. Accept random colors if appearance doesn't matter

Example fix

// before
inferencer = DetInferencer(cfg, weights=w)  # random palette
// after
inferencer = DetInferencer(cfg, weights=w, palette='coco')

palette = palette if palette != 'none' else 'random'  # pre-decide instead of relying on silent default

When it happens

Trigger: Running DetInferencer where palette='none' (default), the test dataset metainfo has no 'palette' key, and the checkpoint/config dataset_meta also lacks palette (e.g. non-COCO dataset without palette in METAINFO).

Common situations: Custom datasets whose METAINFO omits 'palette'; converted checkpoints; results are correct but instance colors vary run to run.

Related errors

• palette does not exist, random is used by default. You can a
• Invalid type for palette: {type(palette)}
• dataset_meta or class names are not saved in the checkpoint'
• Checkpoint is not loaded, and the inference result is calcul
• weights is None, use COCO classes by default.

AI-assisted analysis of open-mmlab/mmdetection@cfd5d3a985 (2026-08-27). Data as JSON: /api/errors/c9910d09e09fd687. Report an issue: GitHub.