| --- |
| license: other |
| task_categories: |
| - video-classification |
| tags: |
| - video |
| - self-supervised |
| - howto100m |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # HowTo1k — a 128-hour HowTo100M subset for video SSL pretraining |
|
|
| A reproduction of the "0.1% of HowTo100M = 128 hours" data-scale cell used in |
| [*Intuitive physics understanding emerges from self-supervised pretraining on natural videos*](https://arxiv.org/abs/2502.11831) |
| (Garrido, Ballas, LeCun), preprocessed for V-JEPA-style training. |
|
|
| | | | |
| |---|---| |
| | Videos | **1,119** (whole videos, not caption clips) | |
| | Total duration | **128.01 h** | |
| | Mean / median length | 412 s / 312 s | |
| | Resolution | short side ≥ 224 (median 360), aspect ratio preserved | |
| | Frame rate | 30 fps (all files) | |
| | Codec | H.264, CRF 23 | |
| | Size | 22 GB | |
|
|
| ## How it was built |
|
|
| 1. **Video list** — `csv/howto100m_videos.csv` from |
| [antoine77340/MIL-NCE_HowTo100M](https://github.com/antoine77340/MIL-NCE_HowTo100M), |
| the authors' mirror manifest: 1,238,791 entries. |
| 2. **Sampling** — uniform draw at a fixed seed (`shuf --random-source`, seed 0, plus a 300-entry |
| top-up at seed 1 from the complement). This follows the paper's protocol, *"sample uniformly |
| X% of the videos"* — whole videos, **not** the ASR caption segments that HowTo100M calls |
| "clips". Those exist for text-video alignment; a vision-only model never reads them, and |
| indexing them would change the sampling distribution from per-video-uniform to |
| per-second-uniform. `sampled.txt` records the full draw in order. |
| 3. **Download** — `yt-dlp`, best stream with `height<=360`. |
| 4. **Transcode** — short side capped at 360 with `min()` so nothing is upsampled, aspect ratio |
| preserved, 30 fps, H.264. **No square centre crop**: V-JEPA does its own |
| `RandomResizedCrop(scale=(0.3,1.0), ratio=(0.75,1.35))`, and pre-squaring would discard 44% |
| of a 16:9 frame first. |
| 5. **Filter** — short side ≥ 224 and ≥ 96 frames (`num_frames 16 × sampling_rate 6`). |
|
|
| ### Why short side 360 |
|
|
| With `RandomResizedCrop(scale=(0.3,1.0), ratio=(0.75,1.35))` targeting 224×224, the fraction of |
| crops that end up **upsampled** depends on the source's short side (measured, 200k samples): |
|
|
| | source short side | 224 | 256 | 288 | 320 | **360** | |
| |---|---|---|---|---|---| |
| | crops upsampled | 98.3% | 64.5% | 33.5% | 8.4% | **0.0%** | |
|
|
| At 224 the crop height can never exceed the source height, so almost every sample is |
| interpolated. 360 removes this; going higher only costs disk and decode time. |
|
|
| ## Known deviations from the paper |
|
|
| - **1,119 videos, not 1,239.** 0.1% of 1,238,791 is 1,239. The *hours* match (128.01 vs 128) |
| because the videos drawn here average 412 s against the ~390 s the paper implies. Collection |
| stopped at 1,119 because ~1,100 downloads in one sitting triggered YouTube's bot check — |
| confirmed by ids already on disk failing too, i.e. throttling rather than link rot. |
| - **Link rot.** Of 2,000 sampled ids, 1,119 were retrieved. The true rot rate is below the |
| implied 44% because later attempts were throttled, not dead. |
| - **Clip length.** Training with `num_frames 16, sampling_rate 6` at 30 fps gives 3.20 s per |
| clip (5.0 effective fps). The paper states 3.0 s / 5.33 fps, but its Table S1 also copies |
| V-JEPA's `sampling_rate=4`, and those two are consistent only at a 21.33 fps source. 30/6 is |
| 6.7% off the stated figure where 30 with stride 4 would be 29% off. |
|
|
| ## Usage |
|
|
| `index.csv` is a space-separated `<relative_path> <label>` list, the format |
| [facebookresearch/jepa](https://github.com/facebookresearch/jepa)'s `VideoDataset` expects. The |
| label column is unused by pretraining and is always 0. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| root = snapshot_download("Ruian7P/HowTo1k", repo_type="dataset") |
| # rewrite index.csv to absolute paths before pointing a trainer at it |
| ``` |
|
|
| ## Licensing |
|
|
| The videos are YouTube content collected via the HowTo100M video list. HowTo100M itself is |
| distributed as identifiers rather than media, and the authors' own video mirror is |
| credential-gated. Redistribution here is for non-commercial research reproduction only; rights |
| remain with the original uploaders. |
|
|
