README.md

---
license: mit
task_categories:
  - visual-question-answering
tags:
  - generative-vqa
  - multimodal
  - vqa-v2
  - coco
  - question-answering
pretty_name: Generative-VQA-V2 (Curated)
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: full
        path: main_metadata.csv
---

# Generative-VQA-V2-Curated

A curated, balanced, and cleaned version of the VQA v2 dataset specifically optimized for **Generative Visual Question Answering**.

This dataset transforms the standard VQA task into a generative challenge by removing "yes/no" shortcuts and balancing answer distributions to prevent model over-fitting on dominant classes.

## Dataset Summary

The primary goal of this curated set is to provide a "clean" signal for training multimodal models by:

- **Eliminating Binary Biases**: Removed all "yes/no" and "unknown" style answers
- **Balancing Classes**: Capped samples at 600 per answer to ensure the model learns a diverse vocabulary
- **Filtering Ambiguity**: Removed generic questions (e.g., "What is this?") to focus on specific visual grounding

## Dataset Statistics

- **Total QA Pairs**: 135,268
- **Unique Answer Classes**: 1,251
- **Source Images**: COCO Train 2014
- **Minimum Frequency per Answer**: 20
- **Maximum Samples per Answer**: 600
- **Average Question Length**: ~6 words
- **Average Answer Length**: ~1.5 words

## Curation Logic

The dataset was generated using the following filtering pipeline:

1. **Consensus-Based**: Only the majority-vote answer from the 10 human annotators is used
2. **Exclusion List**:
   - Boolean answers: `yes`, `no`
   - Uncertainty markers: `unknown`, `none`, `n/a`, `cant tell`, `not sure`
3. **Ambiguity Filter**: Removed questions containing:
   - "what is in the image"
   - "what is this"
   - "what is that"
   - "what do you see"
4. **Conciseness**: Answers are restricted to ≤5 words and ≤30 characters

## Repository Structure

```
Deva8/Generative-VQA-V2-Curated/
├── main_metadata.csv         # ⭐ Primary data file (17 MB)
├── gen_vqa_v2-images.zip     # 📦 Images archive (10.1 GB)
└── README.md
```

### Inside `gen_vqa_v2-images.zip`:

```
gen_vqa_v2-images.zip (10.1 GB)
└── gen_vqa_v2-images/
    └── gen_vqa_v22/
        └── images/
            ├── COCO_train2014_000000004702.jpg
            ├── COCO_train2014_000000012460.jpg
            ├── COCO_train2014_000000183672.jpg
            └── ... (135,268 images total)
```

**Note**: The zip also contains `metadata.csv` and `qa_pairs.json` files which are **not used** by this dataset. Please use `main_metadata.csv` at the repository root instead.

## Download Instructions

### Option 1: Using `huggingface_hub` (Recommended)

```python
from huggingface_hub import hf_hub_download
import zipfile
import os

# Download the images zip file (10.1 GB - will be cached)
zip_path = hf_hub_download(
    repo_id="Deva8/Generative-VQA-V2-Curated",
    filename="gen_vqa_v2-images.zip",
    repo_type="dataset"
)

# Extract to a directory
extract_dir = "./gen_vqa_images"
os.makedirs(extract_dir, exist_ok=True)

print(f"Extracting {zip_path}...")
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
    zip_ref.extractall(extract_dir)

print(f"✓ Images extracted to: {extract_dir}")
print(f"✓ Found {len([f for f in os.listdir(os.path.join(extract_dir, 'gen_vqa_v2-images/gen_vqa_v22/images')) if f.endswith('.jpg')])} images")
```

### Option 2: Manual Download

1. Go to: https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated/tree/main
2. Click on `gen_vqa_v2-images.zip` (10.1 GB)
3. Click the download button
4. Extract the zip file to your working directory

## 🔧 Metadata Fields

The dataset viewer above shows the metadata CSV with the following columns:

| Field | Type | Description |
|-------|------|-------------|
| `image_id` | int64 | Original COCO Image ID |
| `question_id` | int64 | Original VQA v2 Question ID |
| `question` | string | Natural language question about the image |
| `answer` | string | Curated ground-truth answer |
| `file_name` | string | Relative path to image file |

### Example Rows:

```csv
image_id,question_id,question,answer,file_name
429568,429568000,What is behind the street sign?,tree,gen_vqa_v2-images/gen_vqa_v22/images/COCO_train2014_000000429568.jpg
4702,4702000,What is on the man's head?,soccer ball,gen_vqa_v2-images/gen_vqa_v22/images/COCO_train2014_000000004702.jpg
183672,183672001,How old is the man?,20,gen_vqa_v2-images/gen_vqa_v22/images/COCO_train2014_000000183672.jpg
```

## 📜 License & Attribution

This dataset is a derivative work of:
- **VQA v2 Dataset** (Goyal et al., 2017) - [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **COCO Dataset** (Lin et al., 2014) - [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

All derivative work is released under the same **MIT License**.

### Original Papers:

```bibtex
@inproceedings{goyal2017making,
  title={Making the v in vqa matter: Elevating the role of image understanding in visual question answering},
  author={Goyal, Yash and Khot, Tejas and Summers-Stay, Douglas and Batra, Dhruv and Parikh, Devi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6904--6913},
  year={2017}
}

@inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}
```

## 📖 Citation

If you use this dataset in your research or project, please cite:

```bibtex
@misc{devarajan_genvqa_2026,
  author = {Devarajan},
  title = {Generative-VQA-V2-Curated: A Balanced Dataset for Open-Ended Generative VQA},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated}}
}
```

## 🤝 Contributing

Found an issue or have suggestions? Please open a discussion on the HuggingFace dataset page!