---
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](https://huggingface.co/datasets/macrodata/WGO-Bench) ([blog](https://macrodata.co/blog/annotating-robot-video-subtasks)). 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

```json
{
  "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** | **F1@0.75** | Fraction of gold events with IoU ≥ 0.75. Under 1:1 binding with matched counts this equals thresholded precision = recall = F1 — the same F1@0.75 Macrodata uses for free segmentation (here without snap) |

**Takeaway:** IoU is the event-level unit; continuous F1 is the run-level summary of those IoUs. F1@0.75 is the Macrodata-aligned publish-style bar on the same bindings. Always quote continuous F1 and F1@0.75 together when reporting.

Shipped code (this repo):

```text
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
```

```bash
# 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:

```python
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

```python
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](https://huggingface.co/datasets/macrodata/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.