README.md

---
annotations_creators:
- expert-generated
language_creators:
- other
language: en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- combination
task_categories:
- other
task_ids:
- multi-label-classification
pretty_name: CUEBench
configs:
- config_name: clue
  default: true
  data_files:
  - split: train
    path: data/clue/train.jsonl
- config_name: mep
  data_files:
  - split: train
    path: data/mep/train.jsonl
dataset_info:
- config_name: clue
  features:
  - name: id
    dtype: int64
  - name: seq_name
    dtype: string
  - name: frame_count
    dtype: int64
  - name: aligned_id
    dtype: string
  - name: image_id
    dtype: string
  - name: observed_classes
    sequence: string
  - name: target_classes
    sequence: string
  - name: detected_classes
    sequence: string
  - name: image_path
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 1101143
    num_examples: 1648
  download_size: 1101143
  dataset_size: 1101143
- config_name: mep
  features:
  - name: id
    dtype: int64
  - name: seq_name
    dtype: string
  - name: frame_count
    dtype: int64
  - name: aligned_id
    dtype: string
  - name: image_id
    dtype: string
  - name: observed_classes
  - name: image
    dtype: image
    sequence: string
  - name: target_classes
    sequence: string
  - name: detected_classes
    sequence: string
  - name: image_path
    dtype: string
  splits:
  - name: train
    num_bytes: 845579
    num_examples: 1216
  download_size: 845579
  dataset_size: 845579
---

# CUEBench: Contextual Unobserved Entity Benchmark

CUEBench is a neurosymbolic benchmark that emphasizes **contextual entity prediction** in autonomous driving scenes. Unlike traditional detection tasks, CUEBench focuses on reasoning over **unobserved entities** — objects that may be occluded, out-of-frame, or affected by sensor failures.

## Dataset Summary

- **Modalities**: RGB dashcam imagery + symbolic annotations (provided as metadata)
- **Primary task**: Predict unobserved `target_classes` given the set of `observed_classes` in a scene
- **Geography / Scenario**: Urban autonomous driving across diverse traffic densities
- **License**: CC-BY-4.0 (you may adapt if different licensing is desired)

### Configurations

| Config | File | Description |
| --- | --- | --- |
| `clue` *(default)* | `data/clue/train.jsonl` | Contextual Unobserved Entity (CLUE) frames with heavy occlusions and single-target predictions. |
| `mep` | `data/mep/train.jsonl` | Multi-Entity Prediction (MEP) split that introduces complementary metadata and more diverse target sets. |

When this dataset is viewed on Hugging Face, the dataset viewer automatically exposes a **config dropdown** so you can switch between `clue` and `mep` without leaving the UI.

## Dataset Structure

### Data Fields
| Field | Type | Description |
| --- | --- | --- |
| `image_id` | `string` | Unique identifier for each frame (`aligned_id` in the raw metadata).
| `image_path` | `string` | Relative path to the rendered frame image.
| `observed_classes` | `list[string]` | Entity classes detected in-frame (cars, cones, pedestrians, etc.).
| `target_classes` | `list[string]` | Entities inferred to exist but unobserved (occluded, off-frame, sensor failure).

### Splits
Each configuration exposes a single **train** split sourced from either `clue_metadata.jsonl` or `mep_metadata.jsonl`. Feel free to carve out validation/test subsets before upload if you need them.

### Label Taxonomy
Representative classes include: `Car`, `Bus`, `Pedestrian`, `PickupTruck`, `MediumSizedTruck`, `Animal`, `Standing`, `VehicleWithRider`, `ConstructionSign`, `TrafficCone`, and more (~40 classes). Extend this section with the final taxonomy before publication if you want exhaustive documentation.

## Example Record
```json
{
  "image_id": "00003.00019",
  "observed_classes": ["Car", "Bus", "Pedestrian"],
  "target_classes": ["PickupTruck"],
  "image_path": "images/00003.00019.png"
}
```

## Usage

### Loading with `datasets`
```python
from datasets import load_dataset

dataset = load_dataset(
  path="ishwarbb23/cuebench",
  split="train",
  config_name="clue",  # or "mep"
)
```

### Working From Source
```python
from datasets import load_dataset

dataset = load_dataset(
  path="json",
  data_files={"train": "data/clue/train.jsonl"},  # swap with data/mep/train.jsonl
  split="train",
)
```

> **Tip:** From source, you can still switch configurations by pointing `data_files` to `data/mep/train.jsonl`.

### Regenerating viewer files

The repository keeps the original metadata dumps under `raw/`. To refresh the
viewer-friendly JSONL files (e.g. after updating the raw annotations), run:

```bash
/.venv/bin/python scripts/build_viewer_files.py
```

This script adds the derived columns (`image_id`, `observed_classes`, etc.) and
drops the converted files into `data/clue/train.jsonl` and
`data/mep/train.jsonl`. It also updates `data/stats.json`, which is referenced by
the dataset card to keep `dataset_info` counters accurate.

## Metrics

`metric.py` defines **Mean Reciprocal Rank**, **Hits@K (1/3/5/10)**, and **Coverage@K (1/3/5/10)** over the predicted class rankings. When publishing to the Hugging Face Metrics Hub, expose the `compute(predictions, references)` signature so leaderboard integrations can consume it.

## Licensing

The dataset is currently tagged as **CC-BY-4.0**. Update this section if you select a different license.

## Citation

```
@misc{cuebench2025,
  title  = {CUEBench: Contextual Unobserved Entity Benchmark},
  author = {CUEBench Authors},
  year   = {2025}
}
```

## Hugging Face Upload Checklist

1. Install tools: `pip install datasets huggingface_hub` and run `huggingface-cli login`.
2. Create the dataset repo: `huggingface-cli repo create cuebench --type dataset` (or via UI).
3. Ensure directory layout:
   ```
   cuebench/
     README.md
    data/
      clue/train.jsonl
      mep/train.jsonl
    raw/
      clue_metadata.jsonl
      mep_metadata.jsonl
     metric.py           # optional metric script
    scripts/build_viewer_files.py
    scripts/push_to_hub.py
     images/...          # optional or host separately
   ```
4. Initialize Git + LFS:
   ```bash
   cd cuebench
   git init
   git lfs install
   git lfs track "*.jsonl" "images/*"
  git remote add origin https://huggingface.co/datasets/ishwarbb23/cuebench
   git add .
   git commit -m "Initial CUEBench dataset"
   git push origin main
   ```
5. Regenerate viewer files anytime the raw metadata changes: `/.venv/bin/python scripts/build_viewer_files.py`
6. Push the prepared splits to the Hub (per config) using `/.venv/bin/python scripts/push_to_hub.py --repo ishwarbb23/cuebench`
7. On the Hub page, trigger the dataset preview to ensure the loader runs.
8. (Optional) Publish the metric under `metrics/cuebench-metric` following the Metrics Hub template and link it from the dataset card.

Update these steps with any organization-specific tooling you use.