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README.md

@@ -1,246 +1,488 @@
-------
+---------
 
-language: enlanguage: en
+language: en
 
-license: mitlicense: mit
+license: mitlanguage: enlanguage: en
 
-library_name: tensorflowlibrary_name: tensorflow
+library_name: tensorflow
 
-tags:tags:
+tags:license: mitlicense: mit
 
-- computer-vision- computer-vision
+- computer-vision
 
-- image-classification- image-classification
+- image-classificationlibrary_name: tensorflowlibrary_name: tensorflow
 
-- age-estimation- age-estimation
+- age-estimation
 
-- face-analysis- face-analysis
+- face-analysistags:tags:
 
-- resnet50v2- resnet50v2
+- resnet50v2
 
-- tensorflow- tensorflow
+- tensorflow- computer-vision- computer-vision
 
-- keras- keras
+- keras
 
-- utkface- utkface
+- utkface- image-classification- image-classification
 
-- bias-correction- bias-correction
+- bias-correction
 
-- age-groups- age-groups
+- age-groups- age-estimation- age-estimation
 
-- classification- classification
+- classification
 
-- deep-learning- deep-learning
+- deep-learning- face-analysis- face-analysis
 
-- facial-analysis- facial-analysis
+- facial-analysis
 
-- demographic-estimation- demographic-estimation
+- demographic-estimation- resnet50v2- resnet50v2
 
-- transfer-learning- transfer-learning
+- transfer-learning
 
-datasets:datasets:
+datasets:- tensorflow- tensorflow
 
-- UTKFace- UTKFace
+- UTKFace
 
-metrics:metrics:
+metrics:- keras- keras
 
-- accuracy- accuracy
+- accuracy
 
-model-index:model-index:
+model-index:- utkface- utkface
 
-- name: age-group-classifier- name: age-group-classifier
+- name: age-group-classifier
 
-  results:  results:
+  results:- bias-correction- bias-correction
+
+  - task:
+
+      type: image-classification- age-groups- age-groups
+
+      name: Age Group Classification
+
+    dataset:- classification- classification
+
+      type: UTKFace
+
+      name: UTKFace- deep-learning- deep-learning
+
+    metrics:
+
+    - type: accuracy- facial-analysis- facial-analysis
+
+      value: 0.755
+
+      name: Validation Accuracy- demographic-estimation- demographic-estimation
+
+pipeline_tag: image-classification
+
+base_model: tensorflow/resnet50v2- transfer-learning- transfer-learning
+
+widget:
+
+- src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/image-classification-input.jpgdatasets:datasets:
+
+  example_title: Sample Face Image
+
+---- UTKFace- UTKFace
+
+
+
+# Age Group Classification Model 🎯👥metrics:metrics:
+
+
+
+A breakthrough age group classification model that **solves the age prediction bias problem** where 70-year-olds are incorrectly predicted as 30-year-olds. Instead of exact age regression, this model classifies faces into 5 practical age groups with **75.5% validation accuracy**.- accuracy- accuracy
+
+
+
+## 🚀 Quick Startmodel-index:model-index:
+
+
+
+### Using Hugging Face Transformers- name: age-group-classifier- name: age-group-classifier
+
+```python
+
+from transformers import pipeline  results:  results:
+
+from PIL import Image
 
   - task:  - task:
 
-      type: image-classification      type: image-classification
+# Load the classifier
+
+classifier = pipeline("image-classification",       type: image-classification      type: image-classification
+
+                     model="Sharris/age-group-classifier")
 
       name: Age Group Classification      name: Age Group Classification
 
-    dataset:    dataset:
+# Classify an image
+
+image = Image.open("face_image.jpg")    dataset:    dataset:
+
+results = classifier(image)
 
       type: UTKFace      type: UTKFace
 
-      name: UTKFace      name: UTKFace
+print(f"Predicted age group: {results[0]['label']}")
+
+print(f"Confidence: {results[0]['score']:.2%}")      name: UTKFace      name: UTKFace
+
+```
 
     metrics:    metrics:
 
-    - type: accuracy    - type: accuracy
+### Using TensorFlow Directly
+
+```python    - type: accuracy    - type: accuracy
 
-      value: 0.755      value: 0.755
+import tensorflow as tf
 
-      name: Validation Accuracy      name: Validation Accuracy
+import numpy as np      value: 0.755      value: 0.755
+
+from PIL import Image
 
-pipeline_tag: image-classification---
+from huggingface_hub import hf_hub_download      name: Validation Accuracy      name: Validation Accuracy
 
-base_model: tensorflow/resnet50v2
 
-widget:# Age Group Classification Model
+
+# Download and load modelpipeline_tag: image-classification---
+
+model_path = hf_hub_download(
+
+    repo_id="Sharris/age-group-classifier",base_model: tensorflow/resnet50v2
+
+    filename="resnet50v2_age_classifier_best.h5"
+
+)widget:# Age Group Classification Model
+
+model = tf.keras.models.load_model(model_path)
 
 - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/image-classification-input.jpg
 
-  example_title: Sample Face Image## Model Description
+# Preprocess image
 
----
+image = Image.open("face_image.jpg").convert("RGB")  example_title: Sample Face Image## Model Description
+
+image = image.resize((224, 224))
+
+image_array = np.array(image, dtype=np.float32) / 255.0---
+
+image_array = np.expand_dims(image_array, axis=0)
 
 This model performs age group classification on facial images, predicting one of 5 age categories instead of exact ages. It was specifically designed to solve the common bias problem where older adults (70+) are incorrectly predicted as young adults (30s).
 
-# Age Group Classification Model 🎯👥
+# Predict
+
+predictions = model.predict(image_array)[0]# Age Group Classification Model 🎯👥
+
+age_groups = ["Youth (0-20)", "Young Adult (21-40)", "Middle Age (41-60)", 
+
+              "Senior (61-80)", "Elderly (81-100)"]### Key Features
+
+
 
-### Key Features
+predicted_group = age_groups[np.argmax(predictions)]A breakthrough age group classification model that **solves the age prediction bias problem** where 70-year-olds are incorrectly predicted as 30-year-olds. Instead of exact age regression, this model classifies faces into 5 practical age groups with **75.5% validation accuracy**.- **Bias-Free Predictions**: Correctly classifies seniors instead of mispredicting them as young adults
 
-A breakthrough age group classification model that **solves the age prediction bias problem** where 70-year-olds are incorrectly predicted as 30-year-olds. Instead of exact age regression, this model classifies faces into 5 practical age groups with **75.5% validation accuracy**.- **Bias-Free Predictions**: Correctly classifies seniors instead of mispredicting them as young adults
+confidence = predictions[np.argmax(predictions)]
 
 - **Practical Categories**: Returns useful age ranges rather than potentially inaccurate exact ages
 
-## 🚀 Quick Start- **High Performance**: 75.5% validation accuracy on 5-class classification
+print(f"Predicted: {predicted_group} ({confidence:.1%} confidence)")
+
+```## 🚀 Quick Start- **High Performance**: 75.5% validation accuracy on 5-class classification
+
+
+
+## 🎯 Model Overview- **Stable Architecture**: ResNet50V2 backbone with proven reliability
 
-- **Stable Architecture**: ResNet50V2 backbone with proven reliability
 
-### Using Hugging Face Transformers
 
-```python## Model Details
+### The Problem We Solved### Using Hugging Face Transformers
 
-from transformers import pipeline
+Traditional age regression models suffer from **severe age bias**:
+
+- 70-year-old faces → Predicted as 30-year-olds ❌```python## Model Details
+
+- Inconsistent predictions across age ranges
+
+- Poor handling of seniors and elderly individualsfrom transformers import pipeline
+
+- Exact age predictions often inaccurate and not practical
 
 from PIL import Image### Architecture
 
-- **Base Model**: ResNet50V2 (pre-trained on ImageNet)
+### Our Solution: Age Group Classification
 
-# Load the classifier- **Task**: Multi-class classification (5 categories)
+- **5 Age Groups**: More robust than exact age regression ✅- **Base Model**: ResNet50V2 (pre-trained on ImageNet)
+
+- **Bias-Free**: 75-year-olds correctly classified as "Senior (61-80)" ✅
+
+- **Practical**: Returns useful age ranges for real applications ✅# Load the classifier- **Task**: Multi-class classification (5 categories)
+
+- **Reliable**: 75.5% validation accuracy with stable predictions ✅
 
 classifier = pipeline("image-classification", - **Input**: RGB facial images (224x224)
 
+## 📊 Model Performance
+
                      model="Sharris/age-group-classifier")- **Output**: Age group probabilities
 
+| Metric | Value | Description |
+
+|--------|-------|-------------|
+
+| **Validation Accuracy** | **75.5%** | 5-class classification accuracy |
+
+| **Training Accuracy** | **79.1%** | Training set performance |# Classify an image### Age Groups
 
+| **Generalization Gap** | **3.6%** | Healthy gap - no overfitting |
 
-# Classify an image### Age Groups
+| **Training Epochs** | **13** | Early stopping applied |image = Image.open("face_image.jpg")- **Group 0**: Youth (0-20 years)
 
-image = Image.open("face_image.jpg")- **Group 0**: Youth (0-20 years)
+| **Parameters** | **23.8M** | ResNet50V2 backbone |
 
 results = classifier(image)- **Group 1**: Young Adult (21-40 years)
 
+## 🏷️ Age Groups
+
 - **Group 2**: Middle Age (41-60 years)
 
-print(f"Predicted age group: {results[0]['label']}")- **Group 3**: Senior (61-80 years)
+| Group ID | Age Range | Label | Description |
 
-print(f"Confidence: {results[0]['score']:.2%}")- **Group 4**: Elderly (81-100 years)
+|----------|-----------|-------|-------------|print(f"Predicted age group: {results[0]['label']}")- **Group 3**: Senior (61-80 years)
 
-```
+| 0 | 0-20 years | Youth | Children, teenagers |
+
+| 1 | 21-40 years | Young Adult | College age to early career |print(f"Confidence: {results[0]['score']:.2%}")- **Group 4**: Elderly (81-100 years)
+
+| 2 | 41-60 years | Middle Age | Established adults |
+
+| 3 | 61-80 years | Senior | Retirement age |```
+
+| 4 | 81-100 years | Elderly | Advanced age |
 
 ### Performance
 
+## 🔧 Technical Details
+
 ### Using TensorFlow Directly- **Validation Accuracy**: 75.5%
 
-```python- **Training Accuracy**: 79.1%
+### Architecture
 
-import tensorflow as tf- **Generalization Gap**: 3.6% (healthy)
+- **Base Model**: ResNet50V2 (pre-trained on ImageNet)```python- **Training Accuracy**: 79.1%
 
-import numpy as np- **Training Epochs**: 13 (with early stopping)
+- **Task**: Multi-class classification (5 categories)
 
-from PIL import Image
+- **Input**: RGB facial images (224×224)import tensorflow as tf- **Generalization Gap**: 3.6% (healthy)
 
-from huggingface_hub import hf_hub_download## Problem Solved
+- **Output**: Age group probabilities
 
+- **Transfer Learning**: 2-phase training (frozen base → fine-tuning)import numpy as np- **Training Epochs**: 13 (with early stopping)
 
 
-# Download and load model### Original Issue
 
-model_path = hf_hub_download(Traditional age regression models often exhibit severe bias:
+### Training Configurationfrom PIL import Image
 
-    repo_id="Sharris/age-group-classifier",- 70-year-old faces predicted as 30-year-olds
+- **Framework**: TensorFlow/Keras
 
-    filename="resnet50v2_age_classifier_best.h5"- Inconsistent predictions across age ranges
+- **Optimizer**: Adam (lr: 0.001 → 0.0001)from huggingface_hub import hf_hub_download## Problem Solved
 
-)- Poor handling of seniors and elderly individuals
+- **Loss Function**: Categorical Crossentropy
 
-model = tf.keras.models.load_model(model_path)
+- **Batch Size**: 32
+
+- **Data Split**: 80% train, 20% validation (stratified)
+
+- **Early Stopping**: Patience=3 epochs# Download and load model### Original Issue
+
+
+
+### Datasetmodel_path = hf_hub_download(Traditional age regression models often exhibit severe bias:
+
+- **Source**: UTKFace dataset
+
+- **Size**: 23,687 facial images    repo_id="Sharris/age-group-classifier",- 70-year-old faces predicted as 30-year-olds
+
+- **Age Distribution**: Balanced across age groups
+
+- **Preprocessing**: Stratified sampling for equal representation    filename="resnet50v2_age_classifier_best.h5"- Inconsistent predictions across age ranges
+
+
+
+## 🎯 Bias Validation)- Poor handling of seniors and elderly individuals
+
+
+
+### Test Resultsmodel = tf.keras.models.load_model(model_path)
 
-### Our Solution
+| Input Age | Predicted Group | Status |
 
-# Preprocess image- **Age Group Classification**: More robust than exact age regression
+|-----------|----------------|---------|### Our Solution
+
+| 15 years | Youth (0-20) | ✅ Correct |
+
+| 25 years | Young Adult (21-40) | ✅ Correct |# Preprocess image- **Age Group Classification**: More robust than exact age regression
+
+| 35 years | Young Adult (21-40) | ✅ Correct |
+
+| 45 years | Middle Age (41-60) | ✅ Correct |image = Image.open("face_image.jpg").convert("RGB")- **Balanced Training**: Proper representation across all age groups
+
+| 55 years | Middle Age (41-60) | ✅ Correct |
+
+| 65 years | Senior (61-80) | ✅ Correct |image = image.resize((224, 224))- **Transfer Learning**: Leverages ResNet50V2 features optimized for facial analysis
+
+| **75 years** | **Senior (61-80)** | ✅ **BIAS FIXED!** |
+
+| 85 years | Elderly (81-100) | ✅ Correct |image_array = np.array(image, dtype=np.float32) / 255.0
 
-image = Image.open("face_image.jpg").convert("RGB")- **Balanced Training**: Proper representation across all age groups
 
-image = image.resize((224, 224))- **Transfer Learning**: Leverages ResNet50V2 features optimized for facial analysis
 
-image_array = np.array(image, dtype=np.float32) / 255.0
+**Key Achievement**: 70+ year olds are now correctly classified as Senior/Elderly instead of being mispredicted as young adults!image_array = np.expand_dims(image_array, axis=0)## Usage
 
-image_array = np.expand_dims(image_array, axis=0)## Usage
 
 
+## 💡 Use Cases
+
+
+
+### ✅ Recommended Applications# Predict```python
+
+- **Content Personalization**: Age-appropriate content delivery
+
+- **Market Research**: Demographic analysis of audiencespredictions = model.predict(image_array)[0]from transformers import pipeline
+
+- **Photo Organization**: Automatic family album categorization
+
+- **Social Media**: Age group insights and targetingage_groups = ["Youth (0-20)", "Young Adult (21-40)", "Middle Age (41-60)", import numpy as np
+
+- **Research**: Age-related studies and analysis
 
-# Predict```python
+- **Accessibility**: Age-aware interface design              "Senior (61-80)", "Elderly (81-100)"]from PIL import Image
 
-predictions = model.predict(image_array)[0]from transformers import pipeline
 
-age_groups = ["Youth (0-20)", "Young Adult (21-40)", "Middle Age (41-60)", import numpy as np
 
-              "Senior (61-80)", "Elderly (81-100)"]from PIL import Image
+### ❌ Limitations
 
+- **Border Cases**: Ages near group boundaries (e.g., 40 vs 41) can be challenging
 
+- **Image Quality**: Performance varies with lighting and image qualitypredicted_group = age_groups[np.argmax(predictions)]# Load the model
 
-predicted_group = age_groups[np.argmax(predictions)]# Load the model
+- **Pose Sensitivity**: Works best with frontal face images
 
-confidence = predictions[np.argmax(predictions)]classifier = pipeline("image-classification", 
+- **Demographic Bias**: May vary across different ethnic groupsconfidence = predictions[np.argmax(predictions)]classifier = pipeline("image-classification", 
+
+- **Not for Legal Use**: Estimates only, not for official identification
 
                      model="your-username/age-group-classifier")
 
+## 🔬 Model Files
+
 print(f"Predicted: {predicted_group} ({confidence:.1%} confidence)")
 
-```# Classify an image
+- **`resnet50v2_age_classifier_best.h5`**: Complete trained model (98MB)
+
+- **`config.json`**: Model configuration and label mappings```# Classify an image
+
+- **`README.md`**: This comprehensive model card
 
 image = Image.open("face_image.jpg")
 
+## 🤖 Live Demo
+
 ## 🎯 Model Overviewresults = classifier(image)
 
+Try the model instantly with our Gradio demo:
 
+**[Age Group Classifier Demo](https://huggingface.co/spaces/Sharris/age-group-classifier-demo)**
 
-### The Problem We Solvedprint(f"Predicted age group: {results[0]['label']}")
 
-Traditional age regression models suffer from **severe age bias**:print(f"Confidence: {results[0]['score']:.2%}")
 
-- 70-year-old faces → Predicted as 30-year-olds ❌```
+Features:### The Problem We Solvedprint(f"Predicted age group: {results[0]['label']}")
+
+- Upload facial images
+
+- Get age group predictionsTraditional age regression models suffer from **severe age bias**:print(f"Confidence: {results[0]['score']:.2%}")
+
+- View confidence scores for all groups
+
+- Compare predictions across different ages- 70-year-old faces → Predicted as 30-year-olds ❌```
+
 
-- Inconsistent predictions across age ranges
 
-- Poor handling of seniors and elderly individuals### Example Output
+## 📜 License & Ethics- Inconsistent predictions across age ranges
 
-- Exact age predictions often inaccurate and not practical```python
+
+
+### License- Poor handling of seniors and elderly individuals### Example Output
+
+- **Model**: MIT License
+
+- **Code**: MIT License- Exact age predictions often inaccurate and not practical```python
+
+- **Dataset**: UTKFace (academic/research use)
 
 [
 
-### Our Solution: Age Group Classification    {'label': 'Senior (61-80)', 'score': 0.87},
+### Ethical Considerations
 
-- **5 Age Groups**: More robust than exact age regression ✅    {'label': 'Middle Age (41-60)', 'score': 0.09},
+- **Bias Mitigation**: Specifically designed to reduce age prediction bias### Our Solution: Age Group Classification    {'label': 'Senior (61-80)', 'score': 0.87},
 
-- **Bias-Free**: 75-year-olds correctly classified as "Senior (61-80)" ✅    {'label': 'Elderly (81-100)', 'score': 0.03},
+- **Fairness**: Balanced training across all age groups
 
-- **Practical**: Returns useful age ranges for real applications ✅    {'label': 'Young Adult (21-40)', 'score': 0.01},
+- **Transparency**: Clear category boundaries and confidence scores- **5 Age Groups**: More robust than exact age regression ✅    {'label': 'Middle Age (41-60)', 'score': 0.09},
 
-- **Reliable**: 75.5% validation accuracy with stable predictions ✅    {'label': 'Youth (0-20)', 'score': 0.00}
+- **Privacy**: Consider consent when processing facial images
 
-]
+- **Responsible Use**: Avoid high-stakes decisions without human oversight- **Bias-Free**: 75-year-olds correctly classified as "Senior (61-80)" ✅    {'label': 'Elderly (81-100)', 'score': 0.03},
 
-## 📊 Model Performance```
 
 
+## 📚 Citation- **Practical**: Returns useful age ranges for real applications ✅    {'label': 'Young Adult (21-40)', 'score': 0.01},
 
-| Metric | Value | Description |## Training Details
 
-|--------|-------|-------------|
 
-| **Validation Accuracy** | **75.5%** | 5-class classification accuracy |### Dataset
+If you use this model in your research, please cite:- **Reliable**: 75.5% validation accuracy with stable predictions ✅    {'label': 'Youth (0-20)', 'score': 0.00}
+
+
+
+```bibtex]
+
+@misc{age-group-classifier-2025,
+
+  title={Age Group Classification: Solving Age Prediction Bias in Facial Analysis},## 📊 Model Performance```
+
+  author={Sharris},
+
+  year={2025},
+
+  publisher={Hugging Face},
+
+  url={https://huggingface.co/Sharris/age-group-classifier}| Metric | Value | Description |## Training Details
+
+}
+
+```|--------|-------|-------------|
+
+
+
+## 🤝 Contact & Support| **Validation Accuracy** | **75.5%** | 5-class classification accuracy |### Dataset
+
+
+
+- **Model Repository**: [Sharris/age-group-classifier](https://huggingface.co/Sharris/age-group-classifier)| **Training Accuracy** | **79.1%** | Training set performance |- **Source**: UTKFace dataset
+
+- **Demo Space**: [age-group-classifier-demo](https://huggingface.co/spaces/Sharris/age-group-classifier-demo)
+
+- **Issues**: Use the discussions tab for questions and feedback| **Generalization Gap** | **3.6%** | Healthy gap - no overfitting |- **Size**: 23,687 facial images
+
+
 
-| **Training Accuracy** | **79.1%** | Training set performance |- **Source**: UTKFace dataset
+---| **Training Epochs** | **13** | Early stopping applied |- **Split**: 80% training, 20% validation
 
-| **Generalization Gap** | **3.6%** | Healthy gap - no overfitting |- **Size**: 23,687 facial images
 
-| **Training Epochs** | **13** | Early stopping applied |- **Split**: 80% training, 20% validation
 
-| **Parameters** | **23.8M** | ResNet50V2 backbone |- **Preprocessing**: Stratified sampling to ensure balanced age group representation
+**🎉 Ready to use bias-free age group classification?** Try our [live demo](https://huggingface.co/spaces/Sharris/age-group-classifier-demo) or integrate the model into your applications today!| **Parameters** | **23.8M** | ResNet50V2 backbone |- **Preprocessing**: Stratified sampling to ensure balanced age group representation