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End of preview. Expand in Data Studio
V-JEPA Mamba Dataset (v2)
Preprocessed video frames from the Egocentric-10K dataset, formatted for V-JEPA Mamba pretraining.
Dataset Summary
Each video from Egocentric-10K is filtered and clipped:
- Duration filter: Videos < 3 minutes are discarded. Videos > 3 minutes are clipped to the first 3 minutes.
- Frame extraction: 8 fps using ffmpeg → 1,440 frames per video
- Resolution: 384×384 RGB, shortest-side scaling + center crop
- Format: Each video stored as a single Parquet file with columns:
| Column | Type | Description |
|---|---|---|
video_index |
int32 | Unique video index (0-based) |
frame_index |
int16 | Frame position within the video (0-1439) |
frame_bytes |
binary | JPEG-encoded frame (quality 92) |
video_key |
string | Original video identifier from Egocentric-10K |
Dataset Structure
data/
video_00000.parquet # Videos 0-9999
...
_state.json # Resume checkpoint
shard_01/
video_10000.parquet # Videos 10000-19999
...
(Sharded at 9,500 files per directory to respect HF's 10k/dir limit)
Usage
import pyarrow.parquet as pq
from PIL import Image
import io
# Load a video's frames
table = pq.read_table("data/shard_01/video_10238.parquet")
for row in table.to_pylist():
img = Image.open(io.BytesIO(row["frame_bytes"])) # 384×384 RGB
# ... training loop ...
Processing Pipeline
Built with preprocess-v3.py:
- Download + Extract: 8 parallel download threads + 8 extraction threads, decoupled via queue
- Process: All available CPU cores (30 reserved), ffmpeg decode → JPEG encode → Parquet
- Upload: Single commit per chunk via
upload_folder(avoids rate limits)
Resume-safe: _state.json tracks tar-level progress. On restart, fully-done tars are skipped entirely (zero re-download).
Source
builddotai/Egocentric-10K — 19,495 tar shards across 85 factories of egocentric video recordings.
Citation
@misc{vjepa_mamba_dataset,
title={V-JEPA Mamba Dataset: Preprocessed Egocentric Video Frames},
author={Phi-9 Research},
year={2026},
url={https://huggingface.co/datasets/rookierufus/Vjepa_mamba_dataset_v2}
}
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