README.md

---
pretty_name: V-JEPA Reproduce Recipe
license: other
license_name: mixed-upstream-terms
license_link: https://huggingface.co/datasets/BootsofLagrangian/V-JEPA-Reproduce-Recipe/blob/main/README.md#license-and-access
tags:
  - video
  - metadata
  - pretraining
  - v-jepa
  - deduplication
configs:
  - config_name: samples
    data_files:
      - split: train
        path: "samples/**/*.parquet"
    default: true
  - config_name: assets
    data_files:
      - split: train
        path: "assets/**/*.parquet"
  - config_name: memberships
    data_files:
      - split: train
        path: "memberships/**/*.parquet"
  - config_name: dataset_roles
    data_files:
      - split: train
        path: "dataset_roles.parquet"
  - config_name: dataset_stats
    data_files:
      - split: train
        path: "dataset_stats.parquet"
  - config_name: source_coverage
    data_files:
      - split: train
        path: "source_coverage.parquet"
---

# V-JEPA Reproduce Recipe

Private, metadata-only planning surface for reconstructing a large-scale
V-JEPA-style observation-pretraining mixture. This repository does **not**
redistribute source videos and does not grant rights beyond each upstream
dataset's terms.

## Current release

- Manifest ID: `YTWS-CANONICAL-MANIFEST-V1`
- Run ID: `823483f781094a668cf6682828723d16`
- Schema version: `1`
- Source memberships: `485,265,004`
- Exact interval samples: `469,907,459`
- Canonical assets: `82,701,705`
- Parquet shards: `3,292`
- Parquet-only payload: `41,557,148,598` bytes (38.70 GiB)

The manifest combines the 11 currently materialized metadata sources: COIN,
DROID, EPIC-KITCHENS-100, HD-VILA-100M, HowTo100M ID recovery (unattested ID
mirror), InternVid, Kinetics-710, OpenVid-1M, Panda-70M, YouNiverse (structural
HF mirror), and YT-Temporal-180M (unverified mirror). Provenance quality is
recorded per source. Dataset membership is not a statement that every media URL
remains reachable.

## Layout

```text
manifest.json
samples/dedup_bucket=00..ff/*.parquet
assets/dedup_bucket=00..ff/*.parquet
memberships/dataset_id=<name>/dedup_bucket=00..ff/*.parquet
dataset_roles.parquet
dataset_stats.parquet
source_coverage.parquet
```

- `samples`: exact `(platform, video_id, millisecond interval)` identities.
- `assets`: source video or trajectory identities without interval duplication.
- `memberships`: lossless lineage back to each source metadata row.
- `dataset_roles`: the overlapping `broad_in_the_wild`, `instructional`,
  `curated_temporal_action`, `ego_and_synchronized_observation`, and
  `hosted_quality_video` views.
- `dataset_stats`: per-source row, dedup, reservation, and resolvability counts.
- `source_coverage`: included and excluded catalog sources with provenance or
  exclusion reasons.

Captions, action labels, and other source-specific payload tables are not part
of this repository. Membership rows retain `dataset_id`, `source_row_id`, and
`metadata_revision` as lineage keys for consumers that separately acquired the
corresponding upstream payload. The HowTo100M recovery contains IDs only; the
canonical captions/tasks archive was unavailable and cannot be reconstructed
from this release.

## Load with Hugging Face Datasets

Authentication is required because the repository is private; export a token
with read access as `HF_TOKEN`. The repository exposes six named
configurations. `samples` is the default, so both the bare call and an explicit
`"samples"` select the deduplicated interval table. Use `"assets"` and
`"memberships"` for the other large tables; `"dataset_roles"`,
`"dataset_stats"`, and `"source_coverage"` expose the three compact indexes.

The simplest streaming load is:

```python
from datasets import load_dataset

repo_id = "BootsofLagrangian/V-JEPA-Reproduce-Recipe"
samples = load_dataset(repo_id, "samples", split="train", streaming=True)

web_train = samples.filter(
    lambda row: (
        "broad_in_the_wild" in row["roles"]
        and not row["is_reserved"]
        and row["directly_resolvable"]
    )
)
```

For source lineage, load the `memberships` configuration and filter by
`dataset_id`. To minimize shard discovery and assign the 256 hash buckets
directly across workers, address only the required source and bucket paths:

```python
dataset_id = "howto100m"
bucket = "00"
memberships = load_dataset(
    "parquet",
    data_files={
        "train": (
            f"hf://datasets/{repo_id}/memberships/"
            f"dataset_id={dataset_id}/dedup_bucket={bucket}/*.parquet"
        )
    },
    split="train",
    streaming=True,
)
```

The same direct form applies to `samples/dedup_bucket=<bucket>/*.parquet` and
`assets/dedup_bucket=<bucket>/*.parquet`.

`revision=` is optional for ordinary use because `main` is the current release.
For reproducible training, pass the immutable data commit shown below as
`revision="4b9c79918adcb426fa7d0b27056fcde9366357af"` to `load_dataset`, or add
`@<revision>` to an `hf://` URL. The manifest run ID remains inside
`manifest.json` as provenance; it is not part of the Hub path.

The repository-level
[V-JEPA-scale pretraining guide](https://github.com/SNU-PI/delta_jepa/blob/main/docs/reference/VJEPA_SCALE_PRETRAINING.md)
records the media-materialization boundary, capacity estimates, compute
profiles, and launch gates. The guide requires access to the private GitHub
repository; this dataset card remains the data-release contract.

## Exact-dedup boundary

This release removes exact repeated memberships at identical platform, video
identity, and millisecond interval keys. It does not merge merely overlapping
clips and does not claim perceptual or semantic deduplication. Whole-asset
reservation propagation prevents a held-out asset from leaking through another
dataset membership.

## Publication verification

Data commit `4b9c79918adcb426fa7d0b27056fcde9366357af` is the immutable revision for this
release.
The published tree was checked as 3,293 data objects totaling
41,557,605,070 bytes: 3,292 Parquet shards plus `manifest.json`. Every local
object size and SHA-256 matched its remote Hub object. Pinned streaming loads
each returned a row from the `samples`, `assets`, and `memberships`
configurations; the bare call also resolved to the default `samples` config.
The earlier data commit `e3b4f02a422f760995406a0b8e1221e3882ec7d7` is
superseded: its `runs/<run_id>` paths remain available in this Hub repository's
Git history but no longer resolve on `main`.

## Media smoke status

One fixed random membership from each included source was probed locally. Six
of eleven produced MP4 files that passed `ffprobe` and one-frame CPU decode.
Four YouTube-backed rows reached format extraction but failed at the bounded
ffmpeg section-request boundary with HTTP 403; the selected EPIC remote seek
timed out. This tiny smoke is an integration check, not an estimate of live URL
fraction or dataset quality.

## Planned follow-up

- Extend the pinned streaming-load check from the exercised representative
  `samples`, `assets`, and `memberships` shards to all 256 sample and asset
  buckets and every membership partition.
- Add DINOv2-L embeddings and a separately versioned perceptual/retrieval dedup
  layer without rewriting the exact-identity tables.
- Define replacement sampling weights only after coverage, duration, role, and
  availability measurements; do not treat source row counts as mixture weights.
- Retry the same failed YouTube smoke IDs with an official yt-dlp EJS runtime
  and a bounded native-download-then-local-trim fallback.
- Add a range-aware EPIC media adapter and repeat the same selected ID.
- Run a separately registered, bounded, stratified random-asset reachability
  measurement for every included source. Preserve the selected asset as the
  inferential unit, prohibit candidate replacement, and report per-source
  success fractions with the egress, timestamp, adapter, and uncertainty.
- Recover or explicitly retain exclusions for unavailable, gated, or legally
  withheld sources, including YT-Temporal-1B, LVD-142M, SSv2, Ego4D,
  Ego-Exo4D, and WebVid.
- Decide separately whether source-specific captions and annotations should be
  published as joinable private tables. They are not required for the current
  observation-pretraining manifest.
- Keep post-training/evaluation manifests separate from this pretraining
  release; they are intentionally not part of this upload.
- Add video-storage or external-stream execution only as a versioned successor
  after media licensing, availability, codec, and GPU decode policies are
  fixed.

## License and access

The repository must remain private while provenance and redistribution terms
are audited. Each source retains its own license and access restrictions. A
working public URL or community mirror is not, by itself, redistribution
permission for metadata or media.