Shuaijun/D-JEPA-Dataset

D-JEPA decision supervision

Project website · Model repository · Decision-supervision dataset · Code

Download

Download D-JEPA-supervision-v1.zip (487,257,636 bytes; approximately 487 MB) and verify its SHA256 against the manifest. The archive has one top-level D-JEPA-supervision-v1/ folder. It includes candidate inputs, decision supervision, training/calibration identities and recorded results for the completed paper experiments.

hf download Shuaijun/D-JEPA-Dataset D-JEPA-supervision-v1.zip manifest.json \
  --repo-type dataset --local-dir .
python -m zipfile -e D-JEPA-supervision-v1.zip data

After extraction, start with data/D-JEPA-supervision-v1/SCHEMA.md and POPULATIONS.json. Each split has inputs.npz, metadata.json, and, where observed candidate supervision exists, labels.npz.

import numpy as np

with np.load("data/D-JEPA-supervision-v1/pushobj/train/inputs.npz",
             allow_pickle=False) as inputs:
    candidate_ids = inputs["candidate_ids"]
    features = inputs["features"]

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This release is a downloadable ZIP of NPZ arrays, not a single tabular dataset. Automatic Dataset Viewer processing is disabled because it cannot interpret this mixed archive as training rows. This does not restrict access: download the ZIP above or open the Files and versions tab.

Use hf download followed by numpy.load(..., allow_pickle=False) as shown above, rather than datasets.load_dataset("Shuaijun/D-JEPA-Dataset"). The actual task-specific splits and schemas are defined in the archive's POPULATIONS.json and SCHEMA.md; an automatically inferred Hub default/train split does not describe those populations.

Data and protocol

The archive is designed for lightweight learning and cached-decision evaluation. It is not a complete simulator installation or an exhaustive raw-video dataset.

Important data semantics:

• Inference arrays and outcome labels are separate files.
• Granular has sparse supervision: unlabeled candidates are unknown, not failures.
• PushT's fitting pool has module-specific fit/calibration assignments.
• The independent PushT confirmation has 256 starts, distinct from development.
• Corruption-trained PushT has 50 independent confirmation starts repeated under seven known conditions; do not treat its 350 condition rows as independent starts.
• Some tasks provide training features and separate recorded formal results rather than all formal candidate labels. Only actual observed labels are released.

The detailed schema and population inventory are inside the ZIP. No personal data are intentionally included; observations and decision outcomes originate from the recorded simulated tasks. The authors must finalize the dataset license and applicable upstream resource terms before external publication.