The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, 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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
3DIEBench-OOD
Out-of-distribution (OOD) test set for 3DIEBench, designed to evaluate equivariance generalization to unseen rotation ranges.
Dataset Description
3DIEBench-OOD contains rendered views of ShapeNet objects with rotations sampled from ranges not seen during training:
| Training (3DIEBench) | OOD Test (This Dataset) | |
|---|---|---|
| Rotation Range | [-π/2, π/2] | [-π, -π/2] ∪ [π/2, π] |
This allows testing whether models can extrapolate equivariant predictions to OOD rotation angles.
For generation details, see data/3DIEBench/data_generate_ood.py in the seq-JEPA repository.
Structure
| File | Description |
|---|---|
{synset}/{obj}/image_{i}.jpg |
Rendered image (256×256) |
{synset}/{obj}/latent_{i}.npy |
7D latent vector: [yaw, pitch, roll, floor_hue, spot_θ, spot_φ, spot_hue] |
Usage
import numpy as np from PIL import Image
img = Image.open('04330267/8ff18f81de484792f0b94599b4efe81/image_2.jpg') latent = np.load('04330267/8ff18f81de484792f0b94599b4efe81/latent_2.npy')
latent = [yaw, pitch, roll, floor_hue, spot_theta, spot_phi, spot_hue]## Related Resources
- seq-JEPA Code: GitHub
- Project Page: hafezgh.github.io/seq-jepa
- Original 3DIEBench: SIE Repository
Citation
If you use this dataset, please cite:
@inproceedings{ghaemi2025seqjepa, title={seq-{JEPA}: Autoregressive Predictive Learning of Invariant-Equivariant World Models}, author={Ghaemi, Hafez and Muller, Eilif Benjamin and Bakhtiari, Shahab}, booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}, year={2025}, url={https://openreview.net/forum?id=GKt3VRaCU1} }
@inproceedings{garrido2024sie, title={Self-supervised learning of split invariant equivariant representations}, author={Garrido, Quentin and Balestriero, Randall and Najman, Laurent and LeCun, Yann}, booktitle={International Conference on Machine Learning}, year={2024} }
- Downloads last month
- 10