---
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.