Update README.md

README.md

@@ -27,4 +27,182 @@ configs:
     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.
+