Update README.md

README.md

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     dtype: image
   splits:
   - name: train
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+    num_bytes: 22430429
     num_examples: 800
   - name: test
-    num_bytes: 5963659.0
+    num_bytes: 5963659
     num_examples: 200
   download_size: 28358660
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 configs:
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     path: data/train-*
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     path: data/test-*
+license: apache-2.0
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- code
+pretty_name: Household Objects
+size_categories:
+- 1K<n<10K
 ---
+<h1 align="center" style="font-size:60px;">🏠 HouseObj_VQA</h1>
+<h3 align="center">Household Object Visual Question Answering Dataset</h3>
+
+
+## 📌 Dataset Details
+
+### 🧾 Dataset Description
+
+**HouseObj_VQA** is a curated Visual Question Answering (VQA) dataset designed to evaluate the capability of Vision-Language Models (VLMs) in identifying and reasoning about **household objects**.
+
+The dataset focuses exclusively on **commonly used household items**, such as cream containers, coconut oil bottles, carrom boards, utensils, and similar objects. Each image is paired with one or more natural-language questions and corresponding answers that test object recognition, material identification, and brand/manufacturer awareness.
+
+- 👤 **Curated by:** Department of Electronics and Communication Engineering, Bapatla Engineering College, Bapatla  
+- 🏫 **Institutional Contribution:** Academic research and evaluation  
+- 🌐 **Shared by:** Chandrabhuma (Hugging Face)  
+- 🗣️ **Language:** English  
+- 📜 **License:** Apache-2.0  
+
+---
+
+### 🔗 Dataset Sources
+
+- 🧠 **Repository:** https://huggingface.co/datasets/chandrabhuma/HouseObj_VQA  
+- 📄 **Paper:** Not available (dataset contribution)  
+- 🧪 **Demo:** Intended for VLM benchmarking and evaluation  
+
+---
+
+## 🎯 Uses
+
+### ✅ Direct Use
+
+This dataset is intended for:
+- 🧠 Evaluating **Vision-Language Models (VLMs)**
+- 🔍 Testing **household object recognition**
+- 🧱 Assessing **material identification**
+- 🏷️ Brand / manufacturer awareness in visual reasoning
+- 📊 Benchmarking open-ended VQA performance
+
+Typical example questions:
+- ❓ *What is the object in the image?* → **Carrom board**  
+- ❓ *What is the material of the object?* → **Steel**  
+- ❓ *Identify the manufacturer of the object.* → **Cipla**
+
+---
+
+### 🚫 Out-of-Scope Use
+
+This dataset is **not suitable** for:
+- ❌ Surveillance or personal identification
+- ❌ Medical or safety-critical decision making
+- ❌ Fine-grained industrial inspection
+- ❌ Non-household object classification
+
+---
+
+## 🧱 Dataset Structure
+
+Each data sample contains:
+
+| Field | Description |
+|------|------------|
+| 🆔 `id` | Unique sample identifier |
+| 🖼️ `image_name` | Image file name |
+| ❓ `question` | Natural-language question |
+| ✅ `answer` | Ground-truth answer |
+| 🖼️ `image` | Household object image |
+
+### 📊 Dataset Split
+
+- 🟢 **Training set:** 800 images  
+- 🔵 **Test set:** 200 images  
+- 📦 **Total images:** 1000  
+
+---
+
+## 🛠️ Dataset Creation
+
+### 🎯 Curation Rationale
+
+The dataset was created to address the lack of **household-object-centric VQA benchmarks**, especially for evaluating how well modern VLMs understand **everyday objects** encountered in real-world domestic environments.
+
+---
+
+### 📥 Source Data
+
+#### 🔄 Data Collection and Processing
+
+- 📸 Images captured manually using consumer-grade cameras
+- 🧹 Quality filtering to remove blurred or ambiguous images
+- ✍️ Manual question-answer creation
+- 🔍 Verification for correctness and consistency
+
+---
+
+#### 👥 Who are the source data producers?
+
+The data was collected and annotated by undergraduate students under academic supervision as part of a research initiative.
+
+---
+
+## 🏷️ Annotations
+
+### ✏️ Annotation Process
+
+- 👁️ Manual visual inspection of each image
+- 🧠 Question design targeting object identity, material, and brand
+- 📋 Answer verification by multiple contributors
+
+---
+
+### 👤 Annotators
+  👩‍💻 **Dr.B.Chandra Mohan** (Prof, ECE Dept, Bapatla Engineering College, Bapatla-522102) 
+  📧 chandrabhuma@gmail.com  
+  
+  👩‍💻 **Sri K.Sriharsha**  (Asst.Prof, ECE Dept, Bapatla Engineering College, Bapatla-522102) 
+  📧 sriharsha4b2@gmail.com  
+
+- 👩‍💻 **Naralasetty Mounika**  (UG Student , ECE Dept, Bapatla Engineering College, Bapatla-522102) 
+  📧 naralasettymounika@gmail.com  
+
+- 👩‍💻 **Koneti Surya Mounika**  (UG Student, ECE Dept, Bapatla Engineering College, Bapatla-522102) 
+  📧 mounikasurya860@gmail.com  
+
+- 👨‍💻 **Medisetti Harsha Vardhan**  (UG Student, ECE Dept, Bapatla Engineering College, Bapatla-522102) 
+  📧 medisettiharshavardhan19@gmail.com  
+
+- 👨‍💻 **Kurmala Siva Kumar**  (UG Student, ECE Dept, Bapatla Engineering College, Bapatla-522102) 
+  📧 kumarsiva3951@gmail.com  
+
+---
+
+### 🔐 Personal and Sensitive Information
+
+🚫 The dataset **does not contain**:
+- Personal identities
+- Faces
+- Private locations
+- Sensitive or confidential information
+
+All images depict **non-personal household objects only**.
+
+---
+
+## ⚠️ Bias, Risks, and Limitations
+
+- ⚖️ Limited to household objects available in local environments
+- 🌍 Cultural and regional object bias may exist
+- 🧠 Brand recognition depends on visual clarity
+
+---
+
+### 📝 Recommendations
+
+Users should:
+- Combine this dataset with other object-centric datasets for generalization
+- Avoid over-interpreting brand-recognition results
+- Treat this dataset as an **evaluation benchmark**, not exhaustive coverage
+
+---
+
+## 📚 Citation
+
+### 📌 BibTeX
+
+```bibtex
+@dataset{houseobj_vqa_2025,
+  title={HouseObj\_VQA: A Household Object Visual Question Answering Dataset},
+  author={Chandra mohan bhuma, sri harsha k, Naralasetty, Mounika and Koneti, Surya Mounika and Medisetti, Harsha Vardhan and Kurmala, Siva Kumar},
+  year={2025},
+  publisher={Hugging Face}
+}