README.md

---
license: cc-by-sa-4.0
task_categories:
- zero-shot-classification
language:
- en
pretty_name: ' simco-comco'
size_categories:
- 10K<n<100K
---
# **ComCo & SimCo Datasets**
[🔗 GitHub Project Page](https://clip-oscope.github.io/) | [📄 arXiv Paper](https://arxiv.org/abs/2502.19842)

## **Overview**
This repository contains two datasets, **ComCo** and **SimCo**, designed for evaluating multi-object representation in Vision-Language Models (VLMs). These datasets provide controlled environments for analyzing model biases, object recognition, and compositionality in multi-object scenarios.

- **ComCo**: Composed of real-world objects derived from the COCO dataset.
- **SimCo**: Contains simple geometric shapes in structured multi-object settings.

---

## **ComCo Dataset**
The **ComCo** (Complex COCO Objects) dataset consists of images featuring **2 to 5 objects** from the **COCO dataset**. Each zip file contains different arrangements of objects with variations in:
- **Size** (e.g., large vs. small objects)
- **Position** (top-left, middle, bottom-right, etc.)

ComCo is specifically designed to test VLMs on real-world objects, allowing precise control over object placement and ensuring a systematic evaluation of compositional understanding.

## **SimCo Dataset**
The **SimCo** (Simple Compositional Objects) dataset consists of synthetic images featuring **geometric shapes** such as:
- **Cubes**
- **Spheres**
- **Cylinders**
- **Triangles**
- **Pentagons**

SimCo is used to **isolate model biases** by removing real-world semantics, enabling controlled evaluation of how VLMs process object interactions purely based on **size, shape, and position**.

## **Usage**
These datasets are useful for:
- **Analyzing VLM biases** (e.g., preference for larger objects)
- **Compositionality testing** (how models handle multiple objects in images)
- **Zero-shot & fine-tuning tasks** (evaluating robustness of vision-language embeddings)

### **Loading with Hugging Face `datasets` Library**
You can load the dataset directly using:
```python
from datasets import load_dataset

# Load ComCo dataset
comco = load_dataset("clip-oscope/simco-comco", data_dir="ComCo")

# Load SimCo dataset
simco = load_dataset("clip-oscope/simco-comco", data_dir="SimCo")

```
## **Citation**
If you use this dataset in your research, please cite:
```
@inproceedings{abbasi2025clip,
  title={CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation},
  author={Abbasi, Reza and Nazari, Ali and Sefid, Aminreza and Banayeeanzade, Mohammadali and Rohban, Mohammad Hossein and Soleymani Baghshah, Mahdieh},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2025}
}

```