Duplicate from krutrim-ai-labs/IndicVisionBench

Co-authored-by: Ali Faraz <alifaraz@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
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+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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+# Krutrim Community License Agreement Version 1.0
+
+## 1. Definitions
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README.md

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+---
+dataset_info:
+- config_name: mmt
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: topic
+    dtype: string
+  - name: State/UT
+    dtype: string
+  - name: English
+    dtype: string
+  - name: Hindi
+    dtype: string
+  - name: Bengali
+    dtype: string
+  - name: Gujarati
+    dtype: string
+  - name: Kannada
+    dtype: string
+  - name: Malayalam
+    dtype: string
+  - name: Marathi
+    dtype: string
+  - name: Odia
+    dtype: string
+  - name: Punjabi
+    dtype: string
+  - name: Tamil
+    dtype: string
+  - name: Telugu
+    dtype: string
+  - name: source_url
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 14424797
+    num_examples: 106
+  download_size: 13255747
+  dataset_size: 14424797
+- config_name: ocr
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: text
+    dtype: string
+  - name: language
+    dtype: string
+  - name: page_url
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 614014454
+    num_examples: 876
+  download_size: 612223184
+  dataset_size: 614014454
+- config_name: vqa_en
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: topic
+    dtype: string
+  - name: State/UT
+    dtype: string
+  - name: language
+    dtype: string
+  - name: short_q1
+    dtype: string
+  - name: short_a1
+    dtype: string
+  - name: short_q2
+    dtype: string
+  - name: short_a2
+    dtype: string
+  - name: mcq
+    dtype: string
+  - name: mcq_a
+    dtype: string
+  - name: mcq_opt1
+    dtype: string
+  - name: mcq_opt2
+    dtype: string
+  - name: mcq_opt3
+    dtype: string
+  - name: mcq_opt4
+    dtype: string
+  - name: true_false_q
+    dtype: string
+  - name: true_false_a
+    dtype: string
+  - name: long_q
+    dtype: string
+  - name: long_a
+    dtype: string
+  - name: adversarial_question
+    dtype: string
+  - name: adversarial_answer
+    dtype: string
+  - name: source_url
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 1131332865
+    num_examples: 4117
+  download_size: 1127187152
+  dataset_size: 1131332865
+- config_name: vqa_indic
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: topic
+    dtype: string
+  - name: State/UT
+    dtype: string
+  - name: language
+    dtype: string
+  - name: short_q1
+    dtype: string
+  - name: short_a1
+    dtype: string
+  - name: short_q2
+    dtype: string
+  - name: short_a2
+    dtype: string
+  - name: mcq
+    dtype: string
+  - name: mcq_a
+    dtype: string
+  - name: mcq_opt1
+    dtype: string
+  - name: mcq_opt2
+    dtype: string
+  - name: mcq_opt3
+    dtype: string
+  - name: mcq_opt4
+    dtype: string
+  - name: true_false_q
+    dtype: string
+  - name: true_false_a
+    dtype: string
+  - name: long_q
+    dtype: string
+  - name: long_a
+    dtype: string
+  - name: adversarial_question
+    dtype: string
+  - name: adversarial_answer
+    dtype: string
+  - name: source_url
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 276711951
+    num_examples: 1007
+  download_size: 273419974
+  dataset_size: 276711951
+- config_name: vqa_parallel
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: topic
+    dtype: string
+  - name: State/UT
+    dtype: string
+  - name: language
+    dtype: string
+  - name: short_q1
+    dtype: string
+  - name: short_a1
+    dtype: string
+  - name: short_q2
+    dtype: string
+  - name: short_a2
+    dtype: string
+  - name: mcq
+    dtype: string
+  - name: mcq_a
+    dtype: string
+  - name: mcq_opt1
+    dtype: string
+  - name: mcq_opt2
+    dtype: string
+  - name: mcq_opt3
+    dtype: string
+  - name: mcq_opt4
+    dtype: string
+  - name: true_false_q
+    dtype: string
+  - name: true_false_a
+    dtype: string
+  - name: long_q
+    dtype: string
+  - name: long_a
+    dtype: string
+  - name: adversarial_question
+    dtype: string
+  - name: adversarial_answer
+    dtype: string
+  - name: source_url
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 324650384
+    num_examples: 1166
+  download_size: 321701661
+  dataset_size: 324650384
+configs:
+- config_name: mmt
+  data_files:
+  - split: test
+    path: mmt/test-*
+- config_name: ocr
+  data_files:
+  - split: test
+    path: ocr/test-*
+- config_name: vqa_en
+  data_files:
+  - split: test
+    path: vqa_en/test-*
+- config_name: vqa_indic
+  data_files:
+  - split: test
+    path: vqa_indic/test-*
+- config_name: vqa_parallel
+  data_files:
+  - split: test
+    path: vqa_parallel/test-*
+task_categories:
+- visual-question-answering
+language:
+- en
+- hi
+- ta
+- te
+- ml
+- mr
+- gu
+- pa
+- or
+- kn
+- bn
+tags:
+- vision
+- ocr
+- vqa
+- indic
+- benchmark
+- cultural
+- mmt
+- multimodal
+size_categories:
+- 10K<n<100K
+---
+
+
+# IndicVisionBench
+
+[![ICLR 2026](https://img.shields.io/badge/ICLR-2026-blue)](https://openreview.net/forum?id=LmJoLn04iL)
+[![arXiv](https://img.shields.io/badge/arXiv-2511.04727-b31b1b.svg)](https://arxiv.org/abs/2511.04727)
+[![IndicVisionBench-Github](https://img.shields.io/badge/Github-IndicVisionBench-green?logo=github)](https://github.com/ola-krutrim/IndicVisionBench)
+
+This repository contains the dataset for **IndicVisionBench**, introduced in  
+
+**“IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs”**  
+📄 [arXiv:2511.04727](https://arxiv.org/abs/2511.04727)  
+🏛️ Accepted at **ICLR 2026**  
+🔗 OpenReview: https://openreview.net/forum?id=LmJoLn04iL
+
+IndicVisionBench is a **culturally grounded, multilingual vision-language benchmark** designed to evaluate Vision–Language Models (VLMs) on visual understanding tasks in the Indian context. The benchmark focuses on:
+
+- Multilingual Visual Question Answering (VQA)
+- Culturally-aware reasoning
+- Adversarial robustness
+- Parallel cross-lingual consistency
+- Optical Character Recognition (OCR) in Indic scripts
+- Multimodal Machine Translation (MMT)
+
+Unlike generic VQA datasets, IndicVisionBench emphasizes **Indian cultural context, regional diversity, and Indic language coverage**, enabling systematic evaluation of multilingual and culturally-aware VLMs.
+
+---
+
+## Languages Covered
+
+- English
+- Hindi
+- Tamil
+- Telugu
+- Malayalam
+- Marathi
+- Gujarati
+- Punjabi
+- Odia
+- Kannada
+- Bengali
+
+---
+
+## Benchmark Overview
+
+IndicVisionBench consists of five main configurations:
+
+| Config | Task | #Images | Description |
+|--------|------|-----------|-------------|
+| `mmt` | Multimodal Machine Translation | 106 | Image-grounded translations across Indic languages |
+| `ocr` | Optical Character Recognition | 876 | OCR in multiple Indic scripts |
+| `vqa_en` | Visual Question Answering | 4,117 | Culturally grounded VQA in English |
+| `vqa_indic` | Visual Question Answering | 1,007 | Culturally grounded VQA in Indic languages |
+| `vqa_parallel` | Visual Question Answering | 1,166 | Same QA pairs across multiple languages for cross-lingual consistency |
+
+- **Total images across all configs:** 4993
+- **Total questions across VQA En, Indic and Parallel:** (4117 + 1007 + 1166)*6 = 37,740
+
+---
+
+## Subset Descriptions
+
+### 1️⃣ Multimodal Machine Translation (`mmt`)
+
+Image-grounded translation benchmark with aligned captions across multiple Indic languages.
+
+**Features:**
+- `image`
+- `topic`
+- `State/UT`
+- Parallel captions in 11 languages
+- `source_url`
+
+This subset evaluates:
+- Cultural terminology consistency
+- Visual grounding in translation
+
+### 2️⃣ Optical Character Recognition (`ocr`)
+
+OCR dataset consisting of scanned pages in Indic scripts from Wikisource.
+
+**Features:**
+- `image`
+- `text`
+- `language`
+- `page_url`
+
+This subset evaluates OCR capabitilies on Indic scripts/languages.
+
+### 3️⃣ English VQA (`vqa_en`)
+
+Culturally grounded VQA in English.
+
+Each example includes:
+
+- 2 short-answer questions
+- 1 multiple-choice question (4 options)
+- 1 true/false question
+- 1 long-form reasoning question
+- 1 adversarial question
+- Metadata: `topic`, `language`, `State/UT`, 'source_url'
+
+This subset evaluates:
+- Object & scene understanding
+- Cultural knowledge
+- Fine-grained attribute recognition
+- Robustness to false assumptions in the adversarial questions
+
+### 4️⃣ Indic VQA (`vqa_indic`)
+
+Same VQA format as in `vqa_en`, but in Indic languages.
+
+This subset evaluates:
+- Multilingual reasoning
+- Cultural alignment in local languages
+
+### 5️⃣ Parallel VQA (`vqa_parallel`)
+
+Same VQA format as in `vqa_en`. Parallel multilingual QA pairs for the same image.
+
+This subset enables the study of
+- cross-lingual performance of VLMs across 11 languages (English and 10 Indic languages)
+- region-specific strengths or biases
+
+## Usage
+
+All configurations can be loaded using `datasets`:
+
+```python
+from datasets import load_dataset
+
+# Example: load English VQA split
+ds = load_dataset("krutrim-ai-labs/IndicVisionBench", "vqa_en")["test"]
+
+print(ds[0])
+```
+
+The following five configurations/splits are present in the dataset: 
+- mmt
+- ocr
+- vqa_en
+- vqa_indic
+- vqa_parallel
+
+Images are stored directly within the dataset and loaded automatically by 🤗 Datasets.
+
+## Evaluation Dimensions
+
+IndicVisionBench is designed to measure:
+- Scene & contextual understanding
+- Attribute detection
+- Cultural understanding
+- Bias & adversarial robustness
+- Cross-lingual consistency
+- OCR performance
+- Image-grounded translation capability
+
+## Code & Evaluation
+
+The official inference and evaluation codebase for IndicVisionBench is available on GitHub.
+
+**GitHub Repository:**
+[https://github.com/ola-krutrim/IndicVisionBench](https://github.com/ola-krutrim/IndicVisionBench)
+
+The repository provides the complete pipeline for running inference and reproducing benchmark results across all evaluation tracks.
+
+The codebase includes:
+
+- End-to-end inference pipelines for **Vision-Language Models (VLMs)** and **OCR systems**
+- Modular wrappers enabling easy integration of **API-based models** and **open-source models**
+- Evaluation pipelines for all benchmark tasks:
+  - **OCR evaluation**
+  - **Visual Question Answering (VQA)**
+    - Structured questions (MCQ, True/False)
+    - Open-ended questions (short answer, long answer, adversarial)
+  - **Multimodal Machine Translation (MMT)**
+- **LLM-as-a-judge evaluation** for open-ended VQA responses
+- Data generation scripts for constructing a similar multimodal benchmark.
+
+
+### Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{faraz2026indicvisionbench,
+  title={IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs},
+  author={Ali Faraz and Akash and Shaharukh Khan and Raja Kolla and Akshat Patidar and Suranjan Goswami and Abhinav Ravi and Chandra Khatri and Shubham Agarwal},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2026},
+  url={https://openreview.net/forum?id=LmJoLn04iL}
+}
+```

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