Upload README.md with huggingface_hub

README.md

@@ -1,249 +1,260 @@
----
-license: mit
-task_categories:
-- image-classification
-- visual-question-answering
-- zero-shot-image-classification
-tags:
-- fer2013
-- facial-expression-recognition
-- emotion-recognition
-- emotion-detection
-- computer-vision
-- deep-learning
-- machine-learning
-- psychology
-- human-computer-interaction
-- affective-computing
-- quality-enhanced
-- balanced-dataset
-- pytorch
-- tensorflow
-- transformers
-- cv
-- ai
-size_categories:
-- 10K<n<100K
-language:
-- en
-pretty_name: "FER2013 Enhanced: Advanced Facial Expression Recognition Dataset"
-dataset_info:
-  features:
-  - name: sample_id
-    dtype: string
-  - name: image
-    dtype:
-      array:
-        shape: [48, 48]
-        dtype: uint8
-  - name: emotion
-    dtype:
-      class_label:
-        names: ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
-  - name: emotion_name
-    dtype: string
-  - name: quality_score
-    dtype: float32
-  - name: brightness
-    dtype: float32
-  - name: contrast
-    dtype: float32
-  - name: sample_weight
-    dtype: float32
-  splits:
-  - name: train
-    num_bytes: 1000000
-    num_examples: 25000
-  - name: validation
-    num_bytes: 200000
-    num_examples: 5400
-  - name: test
-    num_bytes: 200000
-    num_examples: 5400
-  download_size: 50000000
-  dataset_size: 1400000
-viewer: true
----
-
-# FER2013 Enhanced: Advanced Facial Expression Recognition Dataset
-
-*The most comprehensive and quality-enhanced version of the famous FER2013 dataset for state-of-the-art emotion recognition research and applications.*
-
-## 🎯 Dataset Overview
-
-**FER2013 Enhanced** is a significantly improved version of the landmark FER2013 facial expression recognition dataset. This enhanced version provides AI-powered quality assessment, balanced data splits, comprehensive metadata, and multi-format support for modern machine learning workflows.
-
-### 🚀 Why Choose FER2013 Enhanced?
-
-- **🎯 Superior Quality**: AI-powered quality scoring eliminates poor samples
-- **⚖️ Balanced Training**: Stratified splits with sample weights for optimal learning
-- **📊 Rich Features**: 15+ metadata features including brightness, contrast, edge content
-- **📦 Multiple Formats**: CSV, JSON, Parquet, and native HuggingFace Datasets
-- **🏋️ Production Ready**: Complete with validation, documentation, and ML integration
-- **🔍 Research Grade**: Comprehensive quality metrics for academic and commercial use
-
-### 📈 Dataset Statistics
-
-- **Total Samples**: 35,887 high-quality images
-- **Training Set**: 25,117 samples  
-- **Validation Set**: 5,380 samples
-- **Test Set**: 5,390 samples
-- **Image Resolution**: 48×48 pixels (grayscale)
-- **Emotion Classes**: 7 distinct facial expressions
-- **Quality Score**: 0.436 average (0-1 scale)
-
-## 🎭 Emotion Classes
-
-| Emotion | Count | Percentage |
-|---------|-------|------------|
-| Angry | 4,953 | 13.8% |
-| Disgust | 547 | 1.5% |
-| Fear | 5,121 | 14.3% |
-| Happy | 8,989 | 25.0% |
-| Sad | 6,077 | 16.9% |
-| Surprise | 4,002 | 11.2% |
-| Neutral | 6,198 | 17.3% |
-
-
-## 🔧 Quick Start
-
-### Installation and Loading
-
-```python
-# Install required packages
-pip install datasets torch torchvision transformers
-
-# Load the dataset
-from datasets import load_dataset
-
-dataset = load_dataset("abhilash88/fer2013-enhanced")
-
-# Access splits
-train_data = dataset["train"]
-validation_data = dataset["validation"] 
-test_data = dataset["test"]
-
-print(f"Training samples: {len(train_data):,}")
-print(f"Features: {train_data.features}")
-```
-
-### Basic Usage Example
-
-```python
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Get a sample
-sample = train_data[0]
-
-# Display image and info
-image = sample["image"]
-emotion = sample["emotion_name"]
-quality = sample["quality_score"]
-
-plt.figure(figsize=(6, 4))
-plt.imshow(image, cmap='gray')
-plt.title(f'Emotion: {emotion.capitalize()} | Quality: {quality:.3f}')
-plt.axis('off')
-plt.show()
-
-print(f"Sample ID: {sample['sample_id']}")
-print(f"Emotion: {emotion} (class {sample['emotion']})")
-print(f"Quality Score: {quality:.3f}")
-print(f"Brightness: {sample['brightness']:.1f}")
-print(f"Contrast: {sample['contrast']:.1f}")
-```
-
-### Enhanced Features
-
-Each sample includes the original FER2013 data plus these enhancements:
-
-```python
-{
-    'sample_id': 'fer2013_000001',           # Unique identifier
-    'emotion': 3,                            # Emotion label (0-6)
-    'emotion_name': 'happy',                 # Human-readable emotion
-    'pixels': '50 36 17 ...',               # Original pixel string  
-    'image_base64': 'iVBORw0KGgoAAAANS...',  # Base64 PNG (JSON only)
-    'quality_score': 0.753,                  # AI-computed quality (0-1)
-    'brightness': 127.5,                     # Average pixel brightness
-    'contrast': 45.2,                        # Pixel standard deviation
-    'sample_weight': 1.24,                   # Class balancing weight
-    'edge_score': 0.234,                     # Edge content measure
-    'focus_score': 0.456,                    # Image sharpness
-    'brightness_score': 0.789,               # Brightness balance
-    'pixel_mean': 127.5,                     # Pixel statistics
-    'pixel_std': 45.2,
-    'pixel_min': 0,
-    'pixel_max': 255
-}
-```
-
-### Emotion Labels
-
-- 0: **Angry** - Expressions of anger, frustration, irritation
-- 1: **Disgust** - Expressions of disgust, revulsion, distaste  
-- 2: **Fear** - Expressions of fear, anxiety, worry
-- 3: **Happy** - Expressions of happiness, joy, contentment
-- 4: **Sad** - Expressions of sadness, sorrow, melancholy
-- 5: **Surprise** - Expressions of surprise, astonishment, shock
-- 6: **Neutral** - Neutral expressions, no clear emotion
-
-## 📊 Quality Assessment Methodology
-
-### Quality Score Components
-
-Each image receives a comprehensive quality assessment:
-
-1. **Edge Content Analysis (30% weight)**
-   - Sobel gradient magnitude calculation
-   - Measures facial feature clarity and definition
-   - Higher scores indicate clearer facial boundaries
-
-2. **Contrast Evaluation (30% weight)**
-   - Pixel intensity standard deviation
-   - Assesses visual distinction and dynamic range
-   - Optimal contrast improves feature discrimination
-
-3. **Focus/Sharpness Measurement (25% weight)**
-   - Laplacian variance calculation
-   - Detects blur and image sharpness
-   - Critical for fine-grained emotion detection
-
-4. **Brightness Balance (15% weight)**
-   - Distance from optimal brightness range
-   - Ensures proper illumination conditions
-   - Prevents over/under-exposure issues
-
-## 🎯 Citation
-
-If you use FER2013 Enhanced in your research, please cite:
-
-```bibtex
-@dataset{fer2013_enhanced_2025,
-  title={FER2013 Enhanced: Advanced Facial Expression Recognition Dataset},
-  author={Enhanced by abhilash88},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/datasets/abhilash88/fer2013-enhanced}
-}
-
-@inproceedings{goodfellow2013challenges,
-  title={Challenges in representation learning: A report on three machine learning contests},
-  author={Goodfellow, Ian J and Erhan, Dumitru and Carrier, Pierre Luc and Courville, Aaron and Mehri, Soroush and Raiko, Tapani and others},
-  booktitle={Neural Information Processing Systems Workshop},
-  year={2013}
-}
-```
-
-## 📄 License
-
-This enhanced dataset is released under the **MIT License**.
-
----
-
-**🎭 Ready to build the next generation of emotion recognition systems?**
-
-*Start with `pip install datasets` and `from datasets import load_dataset`*
-
-*Last updated: 13 July 2025*
+---
+license: mit
+task_categories:
+- image-classification
+- visual-question-answering
+- zero-shot-image-classification
+tags:
+- fer2013
+- facial-expression-recognition
+- emotion-recognition
+- emotion-detection
+- computer-vision
+- deep-learning
+- machine-learning
+- psychology
+- human-computer-interaction
+- affective-computing
+- quality-enhanced
+- balanced-dataset
+- pytorch
+- tensorflow
+- transformers
+- cv
+- ai
+size_categories:
+- 10K<n<100K
+language:
+- en
+pretty_name: "FER2013 Enhanced: Advanced Facial Expression Recognition Dataset"
+dataset_info:
+  features:
+  - name: sample_id
+    dtype: string
+  - name: image
+    dtype:
+      _type: Array2D
+      shape: [48, 48]
+      dtype: uint8
+  - name: emotion
+    dtype:
+      _type: ClassLabel
+      names: ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+  - name: emotion_name
+    dtype: string
+  - name: pixels
+    dtype: string
+  - name: usage
+    dtype: string
+  - name: quality_score
+    dtype: float32
+  - name: brightness
+    dtype: float32
+  - name: contrast
+    dtype: float32
+  - name: sample_weight
+    dtype: float32
+  - name: pixel_mean
+    dtype: float32
+  - name: pixel_std
+    dtype: float32
+  - name: pixel_min
+    dtype: uint8
+  - name: pixel_max
+    dtype: uint8
+  - name: edge_score
+    dtype: float32
+  - name: focus_score
+    dtype: float32
+  - name: brightness_score
+    dtype: float32
+  splits:
+  - name: train
+    num_bytes: 15000000
+    num_examples: 25117
+  - name: validation
+    num_bytes: 3200000
+    num_examples: 5380
+  - name: test
+    num_bytes: 3200000
+    num_examples: 5390
+  download_size: 80000000
+  dataset_size: 21400000
+viewer: true
+---
+
+# FER2013 Enhanced: Advanced Facial Expression Recognition Dataset
+
+*The most comprehensive and quality-enhanced version of the famous FER2013 dataset for state-of-the-art emotion recognition research and applications.*
+
+## 🎯 Dataset Overview
+
+**FER2013 Enhanced** is a significantly improved version of the landmark FER2013 facial expression recognition dataset. This enhanced version provides AI-powered quality assessment, balanced data splits, comprehensive metadata, and multi-format support for modern machine learning workflows.
+
+### 🚀 Why Choose FER2013 Enhanced?
+
+- **🎯 Superior Quality**: AI-powered quality scoring eliminates poor samples
+- **⚖️ Balanced Training**: Stratified splits with sample weights for optimal learning
+- **📊 Rich Features**: 15+ metadata features including brightness, contrast, edge content
+- **📦 Multiple Formats**: CSV, JSON, Parquet, and native HuggingFace Datasets
+- **🏋️ Production Ready**: Complete with validation, documentation, and ML integration
+- **🔍 Research Grade**: Comprehensive quality metrics for academic and commercial use
+
+### 📈 Dataset Statistics
+
+- **Total Samples**: 35,887 high-quality images
+- **Training Set**: 25,117 samples  
+- **Validation Set**: 5,380 samples
+- **Test Set**: 5,390 samples
+- **Image Resolution**: 48×48 pixels (grayscale)
+- **Emotion Classes**: 7 distinct facial expressions
+- **Quality Score**: 0.436 average (0-1 scale)
+
+## 🎭 Emotion Classes
+
+| Emotion | Count | Percentage |
+|---------|-------|------------|
+| Angry | 4,953 | 13.8% |
+| Disgust | 547 | 1.5% |
+| Fear | 5,121 | 14.3% |
+| Happy | 8,989 | 25.0% |
+| Sad | 6,077 | 16.9% |
+| Surprise | 4,002 | 11.2% |
+| Neutral | 6,198 | 17.3% |
+
+
+## 🔧 Quick Start
+
+### Installation and Loading
+
+```python
+# Install required packages
+pip install datasets torch torchvision transformers
+
+# Load the dataset
+from datasets import load_dataset
+
+dataset = load_dataset("abhilash88/fer2013-enhanced")
+
+# Access splits
+train_data = dataset["train"]
+validation_data = dataset["validation"] 
+test_data = dataset["test"]
+
+print(f"Training samples: {len(train_data):,}")
+print(f"Features: {train_data.features}")
+```
+
+### Basic Usage Example
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Get a sample
+sample = train_data[0]
+
+# Display image and info
+image = sample["image"]
+emotion = sample["emotion_name"]
+quality = sample["quality_score"]
+
+plt.figure(figsize=(6, 4))
+plt.imshow(image, cmap='gray')
+plt.title(f'Emotion: {emotion.capitalize()} | Quality: {quality:.3f}')
+plt.axis('off')
+plt.show()
+
+print(f"Sample ID: {sample['sample_id']}")
+print(f"Emotion: {emotion} (class {sample['emotion']})")
+print(f"Quality Score: {quality:.3f}")
+print(f"Brightness: {sample['brightness']:.1f}")
+print(f"Contrast: {sample['contrast']:.1f}")
+```
+
+### Enhanced Features
+
+Each sample includes the original FER2013 data plus these enhancements:
+
+```python
+{
+    'sample_id': 'fer2013_000001',           # Unique identifier
+    'emotion': 3,                            # Emotion label (0-6)
+    'emotion_name': 'happy',                 # Human-readable emotion
+    'pixels': '50 36 17 ...',               # Original pixel string  
+    'image_base64': 'iVBORw0KGgoAAAANS...',  # Base64 PNG (JSON only)
+    'quality_score': 0.753,                  # AI-computed quality (0-1)
+    'brightness': 127.5,                     # Average pixel brightness
+    'contrast': 45.2,                        # Pixel standard deviation
+    'sample_weight': 1.24,                   # Class balancing weight
+    'edge_score': 0.234,                     # Edge content measure
+    'focus_score': 0.456,                    # Image sharpness
+    'brightness_score': 0.789,               # Brightness balance
+    'pixel_mean': 127.5,                     # Pixel statistics
+    'pixel_std': 45.2,
+    'pixel_min': 0,
+    'pixel_max': 255
+}
+```
+
+### Emotion Labels
+
+- 0: **Angry** - Expressions of anger, frustration, irritation
+- 1: **Disgust** - Expressions of disgust, revulsion, distaste  
+- 2: **Fear** - Expressions of fear, anxiety, worry
+- 3: **Happy** - Expressions of happiness, joy, contentment
+- 4: **Sad** - Expressions of sadness, sorrow, melancholy
+- 5: **Surprise** - Expressions of surprise, astonishment, shock
+- 6: **Neutral** - Neutral expressions, no clear emotion
+
+## 📊 Quality Assessment Methodology
+
+### Quality Score Components
+
+Each image receives a comprehensive quality assessment:
+
+1. **Edge Content Analysis (30% weight)**
+   - Sobel gradient magnitude calculation
+   - Measures facial feature clarity and definition
+   - Higher scores indicate clearer facial boundaries
+
+2. **Contrast Evaluation (30% weight)**
+   - Pixel intensity standard deviation
+   - Assesses visual distinction and dynamic range
+   - Optimal contrast improves feature discrimination
+
+3. **Focus/Sharpness Measurement (25% weight)**
+   - Laplacian variance calculation
+   - Detects blur and image sharpness
+   - Critical for fine-grained emotion detection
+
+4. **Brightness Balance (15% weight)**
+   - Distance from optimal brightness range
+   - Ensures proper illumination conditions
+   - Prevents over/under-exposure issues
+
+## 🎯 Citation
+
+If you use FER2013 Enhanced in your research, please cite:
+
+```bibtex
+@dataset{fer2013_enhanced_2025,
+  title={FER2013 Enhanced: Advanced Facial Expression Recognition Dataset},
+  author={Enhanced by abhilash88},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/abhilash88/fer2013-enhanced}
+}
+```
+
+## 📄 License
+
+This enhanced dataset is released under the **MIT License**.
+
+---
+
+**🎭 Ready to build the next generation of emotion recognition systems?**
+
+*Start with `pip install datasets` and `from datasets import load_dataset`*
+
+*Last updated: 13 July 2025*