Datasets:
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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}
}
```
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