README.md

---
license: mit
task_categories:
- image-classification
- image-to-text
- zero-shot-image-classification
language:
- en
pretty_name: COLA
size_categories:
- 10K<n<100K
tags:
- compositionality
- vision-language
- visual-genome
- clevr
- paco
configs:
- config_name: multiobjects
  data_files:
  - split: val
    path: data/multiobjects.parquet
- config_name: singleobjects_gqa
  data_files:
  - split: val
    path: data/singleobjects_gqa.parquet
- config_name: singleobjects_clevr
  data_files:
  - split: val
    path: data/singleobjects_clevr.parquet
- config_name: singleobjects_paco
  data_files:
  - split: val
    path: data/singleobjects_paco.parquet
---

# COLA: Compose Objects Localized with Attributes

Self-contained Hugging Face port of the **COLA** benchmark from the paper
["How to adapt vision-language models to Compose Objects Localized with Attributes?"](https://arxiv.org/abs/2305.03689).

- 📄 Paper: https://arxiv.org/abs/2305.03689
- 🌐 Project page: https://cs-people.bu.edu/array/research/cola/
- 💻 Original code & data: https://github.com/ArijitRay1993/COLA

This repository bundles the benchmark annotations as Parquet files and the referenced
images as regular files under `images/`, so the dataset is fully self-contained.

## Dataset Structure

```
.
├── data/
│   ├── multiobjects.parquet
│   ├── singleobjects_gqa.parquet
│   ├── singleobjects_clevr.parquet
│   ├── singleobjects_paco.parquet
│   ├── singleobjects_gqa_labels.json
│   ├── singleobjects_clevr_labels.json
│   └── singleobjects_paco_labels.json
└── images/
    ├── vg/<vg_id>.jpg              # Visual Genome images (multiobjects + GQA)
    ├── clevr/valA/*.png            # CLEVR-CoGenT valA
    ├── clevr/valB/*.png            # CLEVR-CoGenT valB
    ├── coco/val2017/*.jpg          # COCO val2017 (PACO)
    └── coco/train2017/*.jpg        # COCO train2017 (PACO)
```

Image paths stored in parquet are **relative to the repository root**, e.g.
`images/vg/2390970.jpg`. Load them by joining with the local clone / snapshot path.

## Configs / Splits

### `multiobjects` (210 pairs)

A hard image–caption matching task. Each row contains two images and two captions
whose objects/attributes are swapped: caption 1 applies to image 1 (not image 2) and
vice versa.

| Field      | Type   | Description                       |
|------------|--------|-----------------------------------|
| `image1`   | string | Relative path to image 1          |
| `caption1` | string | Caption describing image 1        |
| `image2`   | string | Relative path to image 2          |
| `caption2` | string | Caption describing image 2        |

### `singleobjects_gqa` (2,589 rows), `singleobjects_clevr` (30,000 rows), `singleobjects_paco` (7,921 rows)

Multi-label classification across fixed vocabularies of multi-attribute object
classes (320 for GQA, 96 for CLEVR, 400 for PACO). The label lists live at
`data/singleobjects_<subset>_labels.json`.

| Field                | Type            | Description                                                   |
|----------------------|-----------------|---------------------------------------------------------------|
| `image`              | string          | Relative path to the image                                    |
| `objects_attributes` | string (JSON)   | Objects + attributes annotation (GQA and CLEVR only)          |
| `label`              | list\[int]      | Binary indicator per class (length matches labels vocabulary) |
| `hard_list`          | list\[int]      | Indicator of whether each class is "hard" for this image      |

For a given class, the paper's MAP metric is computed on images where `hard_list == 1`
for that class. See `scripts/eval.py` in the [original repo](https://github.com/ArijitRay1993/COLA)
for the exact metric.

## Loading

```python
from datasets import load_dataset

mo   = load_dataset("array/cola", "multiobjects", split="val")
gqa  = load_dataset("array/cola", "singleobjects_gqa", split="val")
clv  = load_dataset("array/cola", "singleobjects_clevr", split="val")
paco = load_dataset("array/cola", "singleobjects_paco", split="val")
```

To open an image, resolve it against the local snapshot root:

```python
from huggingface_hub import snapshot_download
from PIL import Image
import os

root = snapshot_download("array/cola", repo_type="dataset")
ex = mo[0]
img1 = Image.open(os.path.join(root, ex["image1"]))
img2 = Image.open(os.path.join(root, ex["image2"]))
```

Or, if you've cloned the repo with `git lfs`, just open paths directly:

```python
Image.open(f"{REPO_DIR}/{ex['image1']}")
```

## Licensing / Source notes

- Visual Genome, CLEVR-CoGenT, and COCO images are redistributed here under their
  respective original licenses. Please refer to the upstream datasets:
  - [Visual Genome](https://visualgenome.org/) (CC BY 4.0)
  - [CLEVR-CoGenT](https://cs.stanford.edu/people/jcjohns/clevr/) (CC BY 4.0)
  - [COCO 2017](https://cocodataset.org/) (CC BY 4.0 for annotations; Flickr terms for images)
- The COLA annotations (parquet files and label lists) are released under the MIT
  license, matching the [original COLA repo](https://github.com/ArijitRay1993/COLA).

## Citation

```bibtex
@article{ray2023cola,
  title   = {COLA: How to adapt vision-language models to Compose Objects Localized with Attributes?},
  author  = {Ray, Arijit and Radenovic, Filip and Dubey, Abhimanyu and Plummer, Bryan A. and Krishna, Ranjay and Saenko, Kate},
  journal = {arXiv preprint arXiv:2305.03689},
  year    = {2023}
}
```