InternVid-1M (FLT, recaptioned)

A uniform random 1,000,000-clip subset of InternVid-10M-FLT, packaged as TFDS-compatible TFRecord shards with raw MP4 bytes embedded in every example and an additional long recap_caption field alongside InternVid's original short caption.

• Clips: 1,000,000 (parent: 10,647,325)
• Shards: 206 TFRecord files, ~2 GiB each
• Size: 412 GiB
• Video: H.264/AVC in MP4, short side 256 px, audio track present (unused by most pipelines)

Why this subset exists

The full 10.6M-clip parent is 4.29 TiB, which is awkward for ablations and for anyone who wants a representative slice rather than the whole corpus. This is a statistically uniform 9.39% sample, so aggregate statistics match the parent closely enough to substitute for it in most experiments.

Sampling method (and why it is uniform)

206 of the parent's 2198 whole shards were selected uniformly at random (random.Random(20260725)), with the final shard chosen so the clip count lands on exactly 1,000,000. Shard payloads are byte-identical copies of the parent shards — nothing was re-encoded, so MP4 bytes and all field values are bit-exact.

Whole-shard selection is only equivalent to per-clip sampling if shards are interchangeable, which was verified on the parent before sampling:

Test	Result
Permutation test, between-shard variance of per-shard mean log(duration)	p = 0.91 (observed 0.076 vs i.i.d. null median 0.114)
Per-shard InternVId-FLT_* source-directory composition	chi² max 15.8, df 10 (crit. 18.3) — passes
Per-shard mean umt_score spread	0.011 vs 0.012 predicted under i.i.d.
Per-shard mean aesthetic_score spread	0.289 vs 0.269 predicted under i.i.d.
Source-video clustering	343/343 probed clips had distinct youtube_id, zero adjacent repeats

The 206 selected shards are spread evenly across the parent's shard index (10 equal bins: 20/19/19/26/11/20/21/23/27/20). Read-back of 357 clips from 24 shards of this subset reproduces the parent distribution (duration median 3.92 s vs 3.70 s; identical fps mix; all 11 source subdirectories present).

The exact source→destination shard mapping is in data/1.0.0/shard_provenance.json.

Fields (15 per example)

Field	Type	Description
video_bytes	bytes	Raw MP4 file bytes (H.264, short side 256)
video_size	int64	MP4 size in bytes
mime_type	string	video/mp4
relative_path	string	e.g. InternVId-FLT_4/<yid>_<start>_<end>.mp4
youtube_id	string	Source YouTube video id
index	int64	Row id in the parent dataset (non-contiguous here)
caption	string	InternVid's original short caption
recap_caption	string	Long re-captioned description (median ~347 bytes)
start_sec / end_sec	float32	Clip span within the source video
start_timestamp / end_timestamp	string	Same span as HH:MM:SS.mmm
duration_sec	float32	Clip duration (matches the real video to ~0.0002 s median)
aesthetic_score	float32	Aesthetic predictor score
umt_score	float32	UMT video-text similarity (the "FLT" filter score)

Note: index refers to the parent's row numbering, so it is not contiguous in this subset (observed range 179,080 – 10,634,432). Don't use it as a row counter.

Measured clip statistics

Measured by parsing MP4 atoms directly (mvhd/mdhd/stts). All sampled clips were perfectly CFR (a single stts entry), so these frame rates are exact.

Duration — heavily right-skewed, most clips are short:

min 0.50s   p10 1.13s   p25 1.90s   median 3.70s   p75 9.74s   p90 19.08s   max 110.2s   mean 8.31s

Frame rate:

fps	share
30.00	30%
29.97	30%
25.00	28%
24.00 / 23.98	8%
other (23.78, 24.98, 29.94, 10.00)	4%

Frame count: median 103, p25 49, p75 250, max 3304.

⚠️ Because the median clip is only ~3.7 s, pipelines that sample a fixed number of frames at a low target fps will frequently come up short — e.g. sampling at 1 fps yields a median of just 3–4 frames. Budget for padding and masking, and check your effective frame counts rather than assuming your requested count.

Usage

TFDS (native format)

import tensorflow_datasets as tfds

builder = tfds.builder_from_directory("data/1.0.0")   # after snapshot_download
ds = builder.as_dataset(split="train")

for ex in tfds.as_numpy(ds.take(1)):
    mp4_bytes = ex["video_bytes"]          # feed to decord / PyAV / ffmpeg
    print(len(mp4_bytes), ex["recap_caption"].decode())

Download first (412 GiB — fetch a subset of shards if you don't need it all):

hf download Letian2003/internvid-1M --repo-type dataset --local-dir ./internvid-1M

Grab just a few shards:

hf download Letian2003/internvid-1M --repo-type dataset --local-dir ./internvid-1M \
  --include "data/1.0.0/dataset_info.json" "data/1.0.0/features.json" \
  "data/1.0.0/internvid_10m_flt_packed-train.tfrecord-0000[0-3]-of-00206"

Note that dataset_info.json declares all 206 shards, so TFDS expects them all to be present; for a partial download, read the shards directly with tf.data.TFRecordDataset instead.

Raw TFRecord (no TFDS)

import tensorflow as tf

FEATURES = {
    "video_bytes":   tf.io.FixedLenFeature([], tf.string),
    "recap_caption": tf.io.FixedLenFeature([], tf.string),
    "caption":       tf.io.FixedLenFeature([], tf.string),
    "duration_sec":  tf.io.FixedLenFeature([], tf.float32),
    "umt_score":     tf.io.FixedLenFeature([], tf.float32),
}

ds = tf.data.TFRecordDataset(tf.io.gfile.glob("data/1.0.0/*.tfrecord-*"))
ds = ds.map(lambda r: tf.io.parse_single_example(r, FEATURES))

Dataset structure

data/1.0.0/
├── dataset_info.json                  # TFDS metadata: 206 shards, 1,000,000 examples
├── features.json                      # TFDS feature spec (byte-identical to parent)
├── shard_provenance.json              # src -> dst shard mapping for reproducibility
├── README.txt                         # provenance notes
├── LICENSE
└── internvid_10m_flt_packed-train.tfrecord-{00000..00205}-of-00206

The dataset viewer is not available for this repo: the clips are stored as raw MP4 bytes inside TFRecord containers rather than Parquet/WebDataset.

Limitations

• Not deduplicated by source video. Clips are independent samples, so two clips from the same YouTube video can both appear. Group by youtube_id if you need source-disjoint train/test splits.
• English captions only.
• Inherits any bias and noise present in InternVid-10M-FLT; recap_caption is model-generated and not human-verified.
• No train/validation split is defined — the whole subset is train.

License

cc-by-nc-sa-4.0, inherited from InternVid. Non-commercial use only. The underlying videos remain the property of their original YouTube uploaders.

Citation

Please cite the original InternVid paper:

@article{wang2023internvid,
  title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
  author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Qiao, Yu},
  journal={arXiv preprint arXiv:2307.06942},
  year={2023}
}