Update README.md

README.md

@@ -1,39 +1,19 @@
-
 ---
 license: mit
 task_categories:
-- visual-question-answering
+  - visual-question-answering
 tags:
-- generative-vqa
-- multimodal
-- vqa-v2
+  - generative-vqa
+  - multimodal
+  - vqa-v2
 pretty_name: Generative-VQA-V2 (Curated)
 size_categories:
-- 100K<n<1M
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: 
-    - "main_metadata.csv"      
-    - "gen_vqa_v2-images.zip"
-dataset_info:
-  features:
-    - name: image_id
-      dtype: int64
-    - name: question_id
-      dtype: int64
-    - name: question
-      dtype: string
-    - name: answer
-      dtype: string
-    - name: file_name           
-      dtype: image
+  - 100K<n<1M
 ---
 
 # Generative-VQA-V2-Curated
 
-A curated, balanced, and cleaned version of the VQA v2 dataset specifically optimized for **Generative Visual Question Answering**.
+A curated, balanced, and cleaned version of the VQA v2 dataset specifically optimized for Generative Visual Question Answering.
 
 This dataset transforms the standard VQA task into a generative challenge by removing "yes/no" shortcuts and balancing answer distributions to prevent model over-fitting on dominant classes.
 
@@ -41,75 +21,129 @@ This dataset transforms the standard VQA task into a generative challenge by rem
 
 The primary goal of this curated set is to provide a "clean" signal for training multimodal models by:
 
-1. **Eliminating Binary Biases:** Removed all "yes/no" and "unknown" style answers.
-2. **Balancing Classes:** Capped samples at **600 per answer** to ensure the model learns a diverse vocabulary.
-3. **Filtering Ambiguity:** Removed generic questions (e.g., "What is this?") to focus on specific visual grounding.
+- **Eliminating Binary Biases**: Removed all "yes/no" and "unknown" style answers.
+- **Balancing Classes**: Capped samples at 600 per answer to ensure the model learns a diverse vocabulary.
+- **Filtering Ambiguity**: Removed generic questions (e.g., "What is this?") to focus on specific visual grounding.
 
 ## Dataset Statistics
 
-* **Total QA Pairs:** 135,268
-* **Unique Answer Classes:** 1,251
-* **Source Images:** COCO Train 2014
-* **Minimum Frequency per Answer:** 20
-* **Maximum Samples per Answer:** 600
+- **Total QA Pairs**: 135,268
+- **Unique Answer Classes**: 1,251
+- **Source Images**: COCO Train 2014
+- **Minimum Frequency per Answer**: 20
+- **Maximum Samples per Answer**: 600
 
 ## Curation Logic
 
 The dataset was generated using the following filtering pipeline:
 
-* **Consensus-Based:** Only the majority-vote answer from the 10 human annotators is used.
-* **Exclusion List:** * Boolean: `yes`, `no`
-* Uncertainty: `unknown`, `none`, `n/a`, `cant tell`, `not sure`
-
+1. **Consensus-Based**: Only the majority-vote answer from the 10 human annotators is used.
+2. **Exclusion List**:
+   - Boolean: yes, no
+   - Uncertainty: unknown, none, n/a, cant tell, not sure
+3. **Ambiguity Filter**: Removed questions containing "what is in the image", "what is this", "what is that", or "what do you see".
+4. **Conciseness**: Answers are restricted to 5 words and 30 characters or fewer.
 
-* **Ambiguity Filter:** Removed questions containing "what is in the image", "what is this", "what is that", or "what do you see".
-* **Conciseness:** Answers are restricted to **5 words** and **30 characters** or fewer.
+## Repository Structure
 
-## Structure
-
-```text
+```
 .
-├── images/               # Curated COCO images (JPG)
-├── qa_pairs.json         # Full metadata (JSON)
-├── metadata.csv          # Metadata (CSV) for easy loading
+├── main_metadata.csv         # Metadata (CSV) for loading
+├── gen_vqa_v2-images.zip     # ZIP containing images and additional files
+│   └── gen_vqa_v22/
+│       ├── images/           # 135k+ COCO images (JPG)
+│       ├── metadata.csv      # Original metadata (not used by dataset loader)
+│       └── qa_pairs.json     # Full QA pairs with all annotations
 └── README.md
-
 ```
 
-### Metadata Fields
+## Metadata Fields
+
+- `image_id`: Original COCO Image ID.
+- `question_id`: Original VQA v2 Question ID.
+- `question`: The natural language question.
+- `answer`: The curated ground-truth answer.
+- `file_name`: Path to image relative to extracted zip root.
 
-* `image_id`: Original COCO Image ID.
-* `question_id`: Original VQA v2 Question ID.
-* `question`: The natural language question.
-* `answer`: The curated ground-truth answer.
-* `image_path`: Path relative to the dataset root.
+## Usage
 
-##  Usage
+### Method 1: Load with Manual Extraction (Recommended for Dataset Viewer)
 
-### Loading with Hugging Face `datasets`
+Since the dataset uses a zip file for images, you'll need to manually extract it first:
 
 ```python
 from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+import zipfile
+import os
+
+# Download the zip file
+zip_path = hf_hub_download(
+    repo_id="Deva8/Generative-VQA-V2-Curated",
+    filename="gen_vqa_v2-images.zip",
+    repo_type="dataset"
+)
+
+# Extract it
+extract_dir = "./gen_vqa_data"
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall(extract_dir)
+
+# Now load the dataset
+dataset = load_dataset(
+    "Deva8/Generative-VQA-V2-Curated",
+    data_files="main_metadata.csv"
+)
+
+# The dataset loader will now be able to find the images
+for item in dataset['train']:
+    print(f"Q: {item['question']}")
+    print(f"A: {item['answer']}")
+    # Note: You'll need to manually load images using the file_name path
+```
 
-dataset = load_dataset("Deva8/Generative-VQA-V2-Curated")
+### Method 2: Direct CSV Loading
 
+```python
+import pandas as pd
+from PIL import Image
+import os
+
+# Load metadata
+df = pd.read_csv("hf://datasets/Deva8/Generative-VQA-V2-Curated/main_metadata.csv")
+
+# After extracting gen_vqa_v2-images.zip to a local directory
+base_path = "./gen_vqa_data"
+
+# Load an example
+row = df.iloc[0]
+img_path = os.path.join(base_path, row['file_name'])
+img = Image.open(img_path)
+
+print(f"Question: {row['question']}")
+print(f"Answer: {row['answer']}")
+img.show()
 ```
 
-### Quick Look (Pandas)
+### Check Answer Distribution
 
 ```python
 import pandas as pd
-df = pd.read_csv("metadata.csv")
-print(df['answer'].value_counts().head(10)) # Check balancing
 
+df = pd.read_csv("hf://datasets/Deva8/Generative-VQA-V2-Curated/main_metadata.csv")
+print(df['answer'].value_counts().head(10))  # Top 10 most common answers
 ```
 
+## Known Limitations
+
+**Dataset Viewer**: The HuggingFace dataset viewer may not work automatically because images are stored in a separate zip file. Users should manually extract the zip and load images programmatically as shown above.
+
 ## License & Attribution
 
-This dataset is a derivative work of the **VQA v2 Dataset** and the **COCO Dataset**.
+This dataset is a derivative work of the VQA v2 Dataset and the COCO Dataset.
 
-* **Images:** [COCO Consortium (CC BY 4.0)](https://www.google.com/search?q=https://cocodataset.org/%23termsofuse)
-* **Annotations:** [VQA v2 (CC BY 4.0)](https://visualqa.org/download.html)
+- **Images**: COCO Consortium (CC BY 4.0)
+- **Annotations**: VQA v2 (CC BY 4.0)
 
 ## Citation