--- 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=/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=/*.parquet` and `assets/dedup_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 `@` 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/` 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.