Datasets:

suryadv
/

dynsuperclevr-24-frame-eval-viewer

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label dynsuperclevr-24-frame-eval-viewer@764a29d633f186765974a082be7ada7447172601
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label dynsuperclevr-24-frame-eval-viewer@764a29d633f186765974a082be7ada7447172601

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DynSuperCLEVR 24-frame evaluation viewer bundle

Open the hosted viewer · Download the complete ZIP

The ZIP contains the reusable static viewer and 20 mixture-model examples: 10 cases starting at the 25th percentile of non-zero interaction F1s, plus the 10 highest-scoring cases. These use mixture checkpoint-414's September 4 PX-only predictions, reevaluated September 9 with corrected GT captions, on the actual DynSuperCLEVR validation/test splits (99 + 96). Examples are ranked again using the corrected evaluation. Full semantic names (e.g. yellow mountain bicycle) and the agreed standalone aliases are taken from the new reference labels. This is a selected qualitative audit, not the full benchmark or a training set.

It includes 24 RGB and segmentation frames per case, exact raw predictions, reference labels, saved evaluator matches, per-sample scores, and source hashes. The score is the mean of temporal/input/output F1 at 0.25/0.5/0.75/1.0. manifest.json records the exact selection and caption-provenance hash. The visualization build does not rerun inference or evaluation. These assets, including raw predictions and reference labels, are public.

Run locally

Download visualizer.zip and unzip it.
Open a terminal inside the extracted dyn24-visualizer folder.
Run python3 serve.py (Windows: py serve.py). No pip installation needed.
Open http://127.0.0.1:8127/ in your browser.

The ZIP includes instructions for adding other examples. Its internal checksums.sha256 verifies the extracted files; visualizer.zip.sha256 in this dataset verifies the download itself.

GT crosses, predicted circles, object-specific toggles, and saved caption correspondence are synchronized across both images. Occlusion handling is a display rule, not an alteration of the raw predictions or saved evaluation.

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Total file size:

62.3 MB