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kfkas/ego-extra-imu-benchmark

Ego-EXTRA Inference-friendly v2 manifest for kfkas/ego-extra-imu-benchmark. This repository contains metadata and logical local asset references only. Full source MP4, VRS, CSV, NPZ, or ZIP files are not redistributed here. Tables table rows purpose benchmark 14,644 one row per evaluation question assets 149 one row per synchronized source asset evidence 14,644 one row per question-to-asset time relation The Viewer uses the benchmark split. The… See the full description on the dataset page: https://huggingface.co/datasets/kfkas/ego-extra-imu-benchmark.

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Ego-EXTRA

Inference-friendly v2 manifest for kfkas/ego-extra-imu-benchmark.

This repository contains metadata and logical local asset references only. Full source MP4, VRS, CSV, NPZ, or ZIP files are not redistributed here.

Tables

table	rows	purpose
`benchmark`	14,644	one row per evaluation question
`assets`	149	one row per synchronized source asset
`evidence`	14,644	one row per question-to-asset time relation

The Viewer uses the benchmark split. The assets and evidence tables are separate Parquet/CSV files under data/v2/ because they intentionally have different columns. The common schema version is 2.0. sample_id joins benchmark to evidence, and asset_id joins evidence to assets.

Load for inference

python

from datasets import load_dataset

benchmark = load_dataset("kfkas/ego-extra-imu-benchmark", split="benchmark")
assets = load_dataset("kfkas/ego-extra-imu-benchmark", data_files={"train": "data/v2/assets.parquet"}, split="train")
evidence = load_dataset("kfkas/ego-extra-imu-benchmark", data_files={"train": "data/v2/evidence.parquet"}, split="train")

row = benchmark[0]
asset_rows = assets.filter(lambda item: item["asset_id"] in row["asset_ids"])
evidence_rows = evidence.filter(lambda item: item["sample_id"] == row["sample_id"])

video_relpath, imu_relpath, and frame_relpath are relative logical hints. Map them to the local sensor_vqa/raw/ root before opening media. query_time_raw and evidence_time_raw preserve benchmark-specific wall-clock or day-relative time.

CSV compatibility

CSV exports are available under data/v2/. List-valued fields are JSON strings in CSV and typed lists in Parquet.

Example

A short example clip is included for Viewer and visual sanity checks; it is not the complete source recording.

Sources

—https://fpv-iplab.github.io/Ego-EXTRA/

Follow the upstream license and access terms before downloading referenced media.