README.md

---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: image_name
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 34909987.036
    num_examples: 1177
  - name: test
    num_bytes: 9179938.0
    num_examples: 295
  download_size: 41934761
  dataset_size: 44089925.036
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
pretty_name: Screenspot5G_VQA
license: cc-by-4.0
task_categories:
- visual-question-answering
- image-text-to-text
language:
- en
multimodal:
- image
- text
size_categories:
- 1K<n<10K
---
📱 Screenspot5G VQA Dataset

🧾 Dataset Details

📌 Dataset Description

      Screenspot5G_VQA is a visual question answering (VQA) dataset for mobile screenshot understanding.
      It is designed to evaluate a model’s ability to reason over real smartphone screen content, including UI elements, icons, layout structure, and visible text.
      All images were captured using a real 5G smartphone, ensuring realistic visual characteristics such as screen resolution, font rendering, and UI density.
      Device: OnePlus Nord CE 2 Lite 5G
      Model Number: CPH2381
      Language: English
      License: CC BY 4.0

📊 Dataset Statistics
      Split	Samples	Size
      Train	1,177	~33.3 MB
      Test	295	~8.7 MB
      Total	1,472	~42 MB
👥 Contributors

      🎓 Faculty

          👨‍🏫 Dr. B. Chandra Mohan
          Professor, Dept. of ECE
          Bapatla Engineering College, Bapatla
          
          👨‍🏫 Sri K. Sri Harsha
          Assistant Professor, Dept. of ECE
          Bapatla Engineering College, Bapatla
          
          👨‍🏫 Dr. P. Vinod Babu
          Associate Professor, Dept. of ECE
          Bapatla Engineering College, Bapatla
      
      🎓 Students

          👨‍🎓 Yarramsetty Sindhu
          Undergraduate Student, Dept. of ECE
          Bapatla Engineering College, Bapatla
          
          👨‍🎓 Vasipalli Prasanna
          Undergraduate Student, Dept. of ECE
          Bapatla Engineering College, Bapatla
          
          👨‍🎓 Pilli Harsha Vardhan
          Undergraduate Student, Dept. of ECE
          Bapatla Engineering College, Bapatla
          
          👨‍🎓 Thulava Vamsi
          Undergraduate Student, Dept. of ECE
          Bapatla Engineering College, Bapatla          

🎯 Uses
✅ Direct Use

      This dataset is suitable for:
      
      📱 Mobile UI understanding
      
      👁️ Screenshot-based VQA
      
      🧠 Vision–Language Model (VLM) evaluation
      
      ♿ Accessibility and assistive technologies
      
      🔍 UI element reasoning and screen comprehension

🚫 Out-of-Scope Use

      ### 🚫 Out-of-Scope Use

    - 🔒 **Privacy-invasive monitoring**
    - 
    - ⚠️ **Real-time automated decision-making without human oversight**
    - 
    - 🔠 **OCR-only benchmarking** (the dataset emphasizes reasoning, not just text extraction)

🗂️ Dataset Structure

      Each sample in the dataset contains the following fields:

    - 🆔 **id** *(int64)*  
      Unique sample identifier
    
    - 🖼️ **image_name** *(string)*  
      Filename of the captured screenshot
    
    - ❓ **question** *(string)*  
      Natural language question referring to the screenshot
    
    - ✅ **answer** *(string)*  
      Ground-truth answer corresponding to the question
    
    - 📷 **image** *(image)*  
      Screenshot image used for visual reasoning

The dataset is provided with **🧪 train/test splits** to support **reproducible evaluation**.


🏗️ Dataset Creation

🎯 Curation Rationale

Modern Vision–Language Models (VLMs) often struggle with mobile screen understanding due to:

    - 🧩 **Dense UI layouts**
    - 🔍 **Small icons and fine-grained fonts**
    - 🔤🖼️ **Mixed visual–textual semantics**

**Screenspot5G_VQA** addresses this gap by using 📱 **real-device screenshots**, rather than synthetic UI renders, enabling more realistic evaluation of mobile screen understanding.


📥 Source Data
🔧 Data Collection and Processing

      - 📱 **Screenshots captured manually** from a physical smartphone

    - ❓ **Questions designed to test:**
      - 🧩 **UI comprehension**
      - 🔤 **Text understanding**
      - 👁️🧠 **Visual grounding and reasoning**
    
    - 🤗 **Dataset formatted for Hugging Face compatibility**


👤 Who are the source data producers?
    
    The dataset was created and annotated by the listed contributors using a personal mobile device.
    No automated web scraping or third-party datasets were used.

🏷️ Annotations
✍️ Annotation Process

      - ✍️ **Questions and answers were manually authored**
      - 🎯 **Each question targets a visible element or semantic property of the screen**
      - 🔢 **Single-answer VQA format**


👥 Who are the annotators?

        The contributors listed above performed the annotation and validation.

🔐 Personal and Sensitive Information

        The dataset does not intentionally include personal, private, or sensitive information.
        Screens were curated to avoid identifiable personal data.

⚖️ Bias, Risks, and Limitations

      - 📱 **Screenshots are limited to a single smartphone model**
      - 🤖 **UI design reflects a specific Android ecosystem**
      - 🌐 **Language coverage is limited to English**

📌 Recommendations

      - 🔄 **Combine with datasets from other devices** to improve model generalization
      - 🧠🔤 **Use alongside OCR benchmarks** for comprehensive screen understanding evaluation

📚 Citation

If you use this dataset, please cite:
      B. Chandra Mohan, K. Sri Harsha, P. Vinod Babu, Y. Sindhu, V. Prasanna, P. Harsha Vardhan, and T. Vamsi, 
      “Screenspot5G_VQA: A Visual Question Answering Dataset for Mobile Screenshot Understanding,” 
      Bapatla Engineering College, Dept. of Electronics and Communication Engineering, Bapatla, India, 2026.