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Datasets:
EPFL-VILAB
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TST-Replica

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Tags:
multimodal-learning
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The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Dataset Card for TST-Replica

Dataset Summary

This custom TST-Replica dataset is used in research work "Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality".

  • pretrain/ is a multimodal pretraining dataset collected using Replica simulation environment. It contains RGB images, and 9 additional tokenized modalities.

  • segmentation/train is the associated downstream dataset used to finetune TST pretrained models on semantic segmentation tasks.

  • segmentation/test contains the test dataset used for evaluation/testing on semantic segmentation task. This data corresponds to samples obtained from the test-space itself.

Dataset Structure Pretraining Data 

TST-Replica/
├── pretrain/
│   ├── test_spaces/
│   │   ├── crop_settings/               # Contains .tar shards
│   │   ├── det/                         # Contains .tar shards
│   │   ├── rgb/                         # Contains .tar shards
│   │   ├── tok_canny_edge@224/          # Contains .tar shards
│   │   ├── ...                          # More tokenized feature directories
│   │   └── tok_semseg@224/              # Contains .tar shards
│   └── transfer/
│       ├── crop_settings/               # Contains .tar shards
│       ├── det/                         # Contains .tar shards
│       ├── rgb/                         # Contains .tar shards
│       ├── tok_canny_edge@224/          # Contains .tar shards
│       ├── ...                          # More tokenized feature directories
│       └── tok_semseg@224/              # Contains .tar shards
├── segmentation/
│   ├── train/                          # Training data for segmentation
│   └── test/                           # Test data for segmentation
└── README.md

Dataset Creation

We use Omnidata, to densely sample Replica meshes corresponding to the 5 scenes to build our pre-training dataset. We defer the details of the sampling procedure to Omnidata.

Source Data

Original dataset samples are collected from Omnidata framework.

Citation Information 

@inproceedings{singh2026tst,
            title={Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality},
            author={Kunal Pratap Singh and Ali Garjani and Rishubh Singh and Muhammad Uzair Khattak and Efe Tarhan and Jason Toskov and Andrei Atanov and O{\u{g}}uzhan Fatih Kar and Amir Zamir},
            booktitle={International Conference on Learning Representations (ICLR)},
            year={2026}
        }
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Homepage:
tst-vision.epfl.ch
Repository:
TST official repository
Paper:
Arxiv
Total file size:
33.9 GB

Collection including EPFL-VILAB/TST-Replica