---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: capability
    sequence: string
  - name: imagesource
    dtype: string
  splits:
  - name: Assamese
    num_bytes: 70756682.0
    num_examples: 218
  - name: Bengali
    num_bytes: 70758785.0
    num_examples: 218
  - name: English
    num_bytes: 70625304.0
    num_examples: 218
  - name: Gujarati
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    num_examples: 218
  - name: Hindi
    num_bytes: 70753807.0
    num_examples: 218
  - name: Kannada
    num_bytes: 70782185.0
    num_examples: 218
  - name: Malayalam
    num_bytes: 70798447.0
    num_examples: 218
  - name: Marathi
    num_bytes: 70761412.0
    num_examples: 218
  - name: Odia
    num_bytes: 70770809.0
    num_examples: 218
  - name: Sanskrit
    num_bytes: 70783403.0
    num_examples: 218
  - name: Tamil
    num_bytes: 70810556.0
    num_examples: 218
  - name: Telugu
    num_bytes: 70775865.0
    num_examples: 218
  download_size: 759913301
  dataset_size: 849128683.0
configs:
- config_name: default
  data_files:
  - split: Assamese
    path: data/Assamese-*
  - split: Bengali
    path: data/Bengali-*
  - split: English
    path: data/English-*
  - split: Gujarati
    path: data/Gujarati-*
  - split: Hindi
    path: data/Hindi-*
  - split: Kannada
    path: data/Kannada-*
  - split: Malayalam
    path: data/Malayalam-*
  - split: Marathi
    path: data/Marathi-*
  - split: Odia
    path: data/Odia-*
  - split: Sanskrit
    path: data/Sanskrit-*
  - split: Tamil
    path: data/Tamil-*
  - split: Telugu
    path: data/Telugu-*
license: other
license_name: krutrim-community-license-agreement-version-1.0
license_link: LICENSE.md
extra_gated_heading: Acknowledge license to accept the repository
extra_gated_button_content: Acknowledge license
language:
- as
- hi
- gu
- ml
- te
- ta
- kn
- or
- bn
- en
- mr
- sa
---

# IndicMMVet: Indian Multilingual Translated Dataset For Evaluating Large Vision Language Models for Integrated Capabilities 

- You can find the performance of Chitrarth on IndicMMVet here : [**Paper**](https://arxiv.org/abs/2502.15392) | [**Github**](https://github.com/ola-krutrim/Chitrarth) | [**HuggingFace**](https://huggingface.co/krutrim-ai-labs/Chitrarth)
- Evaluation Scripts of BharatBench is available here : [**Github**](https://github.com/ola-krutrim/BharatBench)

## 1. Introduction  
IndicMMVet is a new dataset designed for evaluating Large Vision-Language Models (LVLMs) on Visual Question Answering (VQA) tasks and focuses on the integration of multiple core VL capabilities. These include **recognition, OCR, knowledge, language generation, spatial awareness, and math**.  

This dataset is built upon **MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities** ([GitHub](https://github.com/yuweihao/MM-Vet)).

---

## 2. Dataset Details  
IndicMMVet consists of **218 samples** spanning **12 Indic languages** along with English. Each sample includes:  
- **Question**: The question about the image.  
- **Capability**: The type of sampling used (recognition, OCR, knowledge, language generation, spatial awareness, and math).  
- **Answer**: The answer.  

### Supported Languages  
- Assamese  
- Bengali  
- English  
- Gujarati  
- Hindi  
- Kannada  
- Malayalam  
- Marathi  
- Odia  
- Sanskrit  
- Tamil  
- Telugu  

---

## 3. How to Use and Run  

You can load the dataset using the `datasets` library:  

```python
from datasets import load_dataset

dataset = load_dataset("krutrim-ai-labs/IndicMMVet")
print(dataset)
```

---

## 4. License
This code repository and the model weights are licensed under the [Krutrim Community License.](LICENSE.md)

## 5. Citation

```
@article{khan2025chitrarth,
  title={Chitrarth: Bridging Vision and Language for a Billion People},
  author={Shaharukh Khan, Ayush Tarun, Abhinav Ravi, Ali Faraz, Akshat Patidar, Praveen Kumar Pokala, Anagha Bhangare, Raja Kolla, Chandra Khatri, Shubham Agarwal},
  journal={arXiv preprint arXiv:2502.15392},
  year={2025}
}

@misc{liu2023improvedllava,
      title={Improved Baselines with Visual Instruction Tuning}, 
      author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
      publisher={arXiv:2310.03744},
      year={2023},
}

@article{yu2023mm,
  title={Mm-vet: Evaluating large multimodal models for integrated capabilities},
  author={Yu, Weihao and Yang, Zhengyuan and Li, Linjie and Wang, Jianfeng and Lin, Kevin and Liu, Zicheng and Wang, Xinchao and Wang, Lijuan},
  journal={arXiv preprint arXiv:2308.02490},
  year={2023}
}

@article{gala2023indictrans2,
  title={Indictrans2: Towards high-quality and accessible machine translation models for all 22 scheduled indian languages},
  author={Gala, Jay and Chitale, Pranjal A and AK, Raghavan and Gumma, Varun and Doddapaneni, Sumanth and Kumar, Aswanth and Nawale, Janki and Sujatha, Anupama and Puduppully, Ratish and Raghavan, Vivek and others},
  journal={arXiv preprint arXiv:2305.16307},
  year={2023}
}
```

## 6. Contact
Contributions are welcome! If you have any improvements or suggestions, feel free to submit a pull request on GitHub.

## 7. Acknowledgement

IndicMMVet is built with reference to the code of the following projects: [MM-Vet](https://github.com/yuweihao/MM-Vet), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!