Datasets:

Nano1337
/

wgo-bench-localization

license: cc-by-nc-sa-4.0
task_categories:
  - video-classification
  - other
pretty_name: WGO-Bench Localization Given Labels
tags:
  - robotics
  - temporal-localization
  - video
  - wgo-bench
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: test
        path: data/test.parquet

WGO-Bench — Localization Given Labels

Self-contained eval for localization given labels: the model is given the gold event labels (shuffled, with multiplicity) and must return one time interval per occurrence. Videos and gold intervals are embedded in each row.

Derived from Macrodata Labs' WGO-Bench (blog). License: CC-BY-NC-SA-4.0. Keep downstream use consistent with Macrodata's attribution and non-commercial / share-alike terms.

Splits

Split	Source ids	Episodes	Gold events
`train`	`dev_80`	80	623
`test`	`heldout_20`	20	120

Construction seed is frozen at 0. Shuffled label_specs and prompt_text are materialized per row so order is byte-stable without re-running the RNG. Split id lists are also under meta/splits/.

Challenge leakage rule: train localization models only on train. Score free-mode segmentation F1@0.75 on the untouched test episodes (or an external set). Do not train on test.

Schema

Each row is one episode:

Field	Type	Description
`id`	string	Episode id
`family`	string	`homer` / `droid` / `galaxea`
`instruction`	string	High-level episode instruction
`split`	string	`train` or `test`
`video`	binary	MP4 bytes
`label_specs`	list	`{label, multiplicity}` in prompt order (post-shuffle)
`gold_segments`	list	`{start_sec, end_sec, label}` in gold order
`prompt_text`	string	Exact localization prompt
`construction`	struct	`{seed, protocol, source}`

Prediction format

{
  "id": "galaxea_002",
  "labels": [
    {
      "label": "pick up the pink stick",
      "intervals": [
        {"label_echo": "pick up the pink stick", "start_sec": 1.2, "end_sec": 3.4}
      ]
    }
  ]
}

Return exactly multiplicity intervals per listed label. Echo the exact label string in label_echo.

Scoring

No gold-aware snapping. Report both layers:

Layer	Metric	Definition
Per event	IoU	Overlap / union of one gold interval vs its matched prediction (within-label 1:1 assignment; unmatched → 0)
Run / split	mean IoU	Mean of those per-event IoUs
Run / split	continuous F1	Same number under this protocol: when the model returns the requested multiplicities, (N_g = N_p), so continuous F1 (= 2S/(N_g+N_p)) collapses to mean IoU. Emitted as `continuous_f1` (= `mean_iou`) for consistency with free-segmentation continuous F1
Run / split	accuracy@0.5 / @0.75	Fraction of gold events with IoU ≥ threshold

Takeaway: IoU is the event-level unit; continuous F1 is the run-level summary of those IoUs (not a different scoring rule). Always quote both names when reporting, and keep accuracy@0.75 as the strict headline next to them.

Shipped code (this repo):

localization/construct.py   # rebuild specs + prompt from gold
localization/score.py       # interval IoU, assignment, score_episode, summarize
localization/verify.py      # assert parquet specs/prompts match construct()
scripts/score_predictions.py

# from the dataset root (after cloning or downloading the repo files)
python scripts/score_predictions.py \
  --data data/train.parquet \
  --preds my_preds.jsonl

Or with 🤗 Datasets:

from datasets import load_dataset
ds = load_dataset("Nano1337/wgo-bench-localization")
row = ds["train"][0]
print(row["id"], row["label_specs"][:2])
# row["video"] is raw MP4 bytes

Reproduce construction

from localization.construct import label_specs_from_segments, localization_prompt
from localization.schema import GoldSegment
from localization.verify import verify_row

gold = [GoldSegment(**s) for s in row["gold_segments"]]
specs = label_specs_from_segments(row["id"], gold, seed=0)
assert [s.to_dict() for s in specs] == list(row["label_specs"])
assert localization_prompt(row["instruction"], specs) == row["prompt_text"]
verify_row(row)

Attribution

Videos and gold annotations: Macrodata Labs' WGO-Bench, CC-BY-NC-SA-4.0.
This repository adds the localization-given-labels protocol (frozen shuffled label lists, prompts, splits, and scorer). Model predictions are not included.