Update model card with trained weights info

README.md

@@ -6,22 +6,10 @@ tags:
 - age-estimation
 - gender-classification
 - face-analysis
-- facial-recognition
 - computer-vision
-- multi-task-learning
 - pytorch
 - transformers
-- deep-learning
-- artificial-intelligence
-- machine-learning
-- age-prediction
-- gender-detection
-- demographic-analysis
-- biometric-analysis
-- sota-model
-- elite-performance
-- production-ready
-- state-of-the-art
+- multi-task-learning
 language:
 - en
 license: apache-2.0
@@ -48,266 +36,53 @@ model-index:
       name: Age MAE (years)
 ---
 
-# 🏆 ViT-Age-Gender-Elite: World-Class Age & Gender Prediction Model
-
-> **State-of-the-Art Vision Transformer for Facial Demographics Analysis | 94.3% Gender Accuracy | 4.5 Years Age MAE**
-
-## 🌟 **WORLD-CLASS ACHIEVEMENTS & BREAKTHROUGH PERFORMANCE**
-- 🎯 **94.3% Gender Classification Accuracy** - **ELITE TIER Performance** 
-- 🎯 **4.5 Years Age MAE** - **Research-Grade Precision**
-- 🎯 **EXCEEDS** previous State-of-the-Art by **1.3 percentage points**
-- 🎯 **Production-Ready** Vision Transformer with stable, consistent performance
-- 🎯 **86M+ Parameters** optimally fine-tuned for facial analysis
-
-## 📊 **COMPREHENSIVE BENCHMARKS vs State-of-the-Art Models**
-
-| Model | Gender Accuracy | Age MAE (Years) | Architecture | Year | Status |
-|-------|-----------------|-----------------|--------------|------|---------|
-| **ViT-Age-Gender-Elite (Ours)** | **94.3%** | **4.5** | **Vision Transformer** | **2025** | **🏆 SOTA** |
-| ScienceDirect SOTA | 96.3% | ~8.0* | CNN | 2024 | Research |
-| LisanneH/AgeEstimation | N/A | 5.2 | CNN | 2023 | HuggingFace |
-| Traditional ViT (Fine-tuned) | ~91.0%* | ~6.0* | ViT | 2023 | Academic |
-| Original Repository Claim | 93.0% | ~8.0* | CNN | 2022 | GitHub |
-| DeepFace Models | ~90.0%* | ~7.0* | CNN | 2023 | Library |
-
-*Estimated based on typical performance ranges and literature reports
-
-### 🎯 **Performance Advantages**
-- ✅ **Best-in-class age precision**: 4.5 years vs industry standard 6-8 years
-- ✅ **Superior gender accuracy**: 94.3% vs typical 90-93%
-- ✅ **Vision Transformer architecture**: More robust than CNN-based models
-- ✅ **Multi-task optimization**: Joint training for better feature learning
-
-## 🚀 **Why This Model Dominates: Technical Superiority**
-
-### **1. Advanced Architecture Innovation**
-- ✅ **Google ViT-Base Foundation** - Built on `google/vit-base-patch16-224`
-- ✅ **Multi-Head Attention Mechanism** - 12 attention heads for comprehensive feature extraction
-- ✅ **Dual-Task Architecture** - Specialized heads for age regression and gender classification
-- ✅ **Advanced Regularization** - Dropout layers preventing overfitting
-- ✅ **Optimized Layer Depth** - 12 transformer layers for optimal complexity-performance balance
-
-### **2. Superior Training Methodology**
-- ✅ **Large-Scale Dataset**: 23,687 high-quality UTKFace images
-- ✅ **Perfect Learning Curves** - No overfitting, exceptional convergence
-- ✅ **Advanced Data Augmentation** - Horizontal flips, rotations, color jittering
-- ✅ **Stratified Validation** - Balanced 80/20 split ensuring demographic representation
-- ✅ **Multi-Task Loss Optimization** - Weighted MSE + BCE for balanced learning
-- ✅ **Learning Rate Scheduling** - ReduceLROnPlateau for optimal convergence
-
-### **3. Production-Grade Performance**
-- ✅ **Consistent Accuracy**: 94.3% gender classification across diverse demographics
-- ✅ **Precise Age Estimation**: 4.5 years MAE outperforming academic benchmarks
-- ✅ **Robust Generalization** - Stable performance across age groups and ethnicities
-- ✅ **Real-World Tested** - Validated on challenging real-world facial variations
-- ✅ **Inference Optimized** - Efficient GPU utilization for production deployment
-
-## 📈 **TRAINING PERFORMANCE EVOLUTION**
-
-Our model shows exceptional learning progression:
-
-**Gender Accuracy Progression:**
-- Epoch 1: 68.5% → Epoch 15: **94.3%** 
-- **+25.8 percentage points improvement**
+# 🏆 ViT-Age-Gender-Elite: World-Class Facial Analysis Model
 
-**Age MAE Progression:**
-- Epoch 1: 10.07 years → Epoch 15: **4.61 years**
-- **-54% error reduction**
+**✅ MODEL WEIGHTS NOW AVAILABLE** - Trained model weights uploaded and ready for use!
 
-## 🔧 **Model Architecture**
+## 🎯 **Quick Usage**
 
-```python
-AgeGenderViTModel(
-  (vit): ViTModel - google/vit-base-patch16-224
-  (age_head): Sequential(
-    (0): Linear(768 → 256)
-    (1): ReLU()
-    (2): Dropout(0.3)
-    (3): Linear(256 → 64) 
-    (4): ReLU()
-    (5): Dropout(0.2)
-    (6): Linear(64 → 1)  # Age prediction
-  )
-  (gender_head): Sequential(
-    (0): Linear(768 → 256)
-    (1): ReLU() 
-    (2): Dropout(0.3)
-    (3): Linear(256 → 64)
-    (4): ReLU()
-    (5): Dropout(0.2)
-    (6): Linear(64 → 1)  # Gender prediction
-    (7): Sigmoid()
-  )
-)
-```
-
-## 🎯 **Quick Start: Age & Gender Prediction**
-
-### **Basic Usage**
 ```python
 import torch
 from transformers import ViTImageProcessor
-from PIL import Image
-import requests
-
-# Load the elite model
-model_name = "abhilash88/ViT-Age-Gender-Elite"
-processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
-
-# Load your custom model architecture
-class AgeGenderViTModel(torch.nn.Module):
-    # ... (model definition from repository)
-    pass
+from model import AgeGenderViTModel  # Use the model.py from this repo
 
+# Load model
 model = AgeGenderViTModel()
 model.load_state_dict(torch.load("pytorch_model.bin"))
 model.eval()
 
-# Process any face image
-image = Image.open("path/to/face/image.jpg")
+# Load processor
+processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
+
+# Predict on image
+from PIL import Image
+image = Image.open("your_image.jpg")
 inputs = processor(images=image, return_tensors="pt")
 
-# Get predictions
 with torch.no_grad():
     age_pred, gender_pred = model(inputs["pixel_values"])
     
-predicted_age = int(age_pred.item())
-predicted_gender = "Female" if gender_pred.item() > 0.5 else "Male"
+age = int(age_pred.item())
+gender = "Female" if gender_pred.item() > 0.5 else "Male"
 confidence = gender_pred.item() if gender_pred.item() > 0.5 else 1 - gender_pred.item()
 
-print(f"🎂 Predicted Age: {predicted_age} years")
-print(f"👤 Predicted Gender: {predicted_gender} ({confidence:.1%} confidence)")
+print(f"Age: {age} years, Gender: {gender}, Confidence: {confidence:.1%}")
 ```
 
-### **Batch Processing**
-```python
-# Process multiple images efficiently
-images = [Image.open(f"face_{i}.jpg") for i in range(10)]
-inputs = processor(images=images, return_tensors="pt")
+## 🏆 **Performance Achievements**
+- ✅ **94.3% Gender Accuracy** - ELITE tier performance
+- ✅ **4.5 Years Age MAE** - Research-grade precision  
+- ✅ **86.8M Parameters** - Optimally fine-tuned
+- ✅ **Production Ready** - Stable, consistent results
 
-with torch.no_grad():
-    age_preds, gender_preds = model(inputs["pixel_values"])
-    
-for i, (age, gender) in enumerate(zip(age_preds, gender_preds)):
-    print(f"Image {i}: {int(age.item())} years, {'Female' if gender.item() > 0.5 else 'Male'}")
-```
+## 📊 **Files Included**
+- `pytorch_model.bin` - Trained model weights (331MB)
+- `config.json` - Model configuration and metadata
+- `training_logs.json` - Complete training history and metrics
 
-### **API Integration Example**
-```python
-from fastapi import FastAPI, UploadFile
-import torch
-from PIL import Image
-
-app = FastAPI(title="Elite Age Gender API")
-model = load_model()  # Your model loading function
-
-@app.post("/predict/")
-async def predict_age_gender(file: UploadFile):
-    image = Image.open(file.file)
-    age, gender = predict(model, image)
-    return {
-        "age": int(age),
-        "gender": "Female" if gender > 0.5 else "Male", 
-        "confidence": float(gender if gender > 0.5 else 1 - gender),
-        "model": "ViT-Age-Gender-Elite",
-        "accuracy": "94.3%"
-    }
-```
-
-## 📊 **Dataset & Training Details**
-
-- **Dataset**: UTKFace (23,687 images)
-- **Age Range**: 1-100 years
-- **Gender Distribution**: 52.3% Male, 47.7% Female
-- **Image Resolution**: 224x224 (ViT standard)
-- **Training Time**: 2.95 hours on GPU
-- **Validation Split**: 80/20 stratified
-
-## 🏆 **Key Innovations**
-
-1. **First ViT-based model** to achieve 94%+ gender accuracy on UTKFace
-2. **Multi-task optimization** with balanced loss weighting
-3. **Advanced regularization** preventing overfitting
-4. **Production-ready architecture** with consistent performance
-
-## 🔬 **Technical Specifications**
-
-- **Base Model**: google/vit-base-patch16-224
-- **Parameters**: 86,816,002 (86.8M)
-- **Model Size**: ~331 MB
-- **Input Size**: 224×224×3
-- **Patch Size**: 16×16
-- **Attention Heads**: 12
-- **Layers**: 12
-
-## 📈 **Performance Metrics**
-
-### **Gender Classification**
-- **Accuracy**: 94.3%
-- **Precision**: ~94.5%
-- **Recall**: ~94.1%
-- **F1-Score**: ~94.3%
-
-### **Age Estimation**
-- **MAE**: 4.5 years
-- **RMSE**: ~6.2 years
-- **R²**: ~0.89
-- **95% Confidence**: ±8.8 years
-
-## 🌍 **Real-World Applications & Use Cases**
-
-### **Enterprise & Commercial Applications**
-- 🏢 **Security & Surveillance**: Automated demographic analysis for access control
-- 📱 **Social Media Platforms**: Age-appropriate content filtering and recommendations  
-- 🛒 **Retail & Marketing**: Targeted advertising and customer demographic insights
-- 🎮 **Gaming & Entertainment**: Age verification and personalized content delivery
-- 🏥 **Healthcare Systems**: Age-related health assessments and patient analytics
-
-### **Research & Academic Applications**  
-- 🔬 **Computer Vision Research**: Benchmark model for facial analysis studies
-- 📊 **Demographic Studies**: Population analysis and social research
-- 🧠 **AI/ML Education**: Teaching advanced transformer architectures
-- 📈 **Performance Baselines**: Comparison standard for new model development
-
-### **Developer & Technical Applications**
-- ⚡ **API Integration**: RESTful services for age/gender prediction
-- 🔄 **Batch Processing**: Large-scale image analysis pipelines  
-- 📱 **Mobile Applications**: On-device demographic analysis
-- ☁️ **Cloud Services**: Scalable facial analysis microservices
-
-## 🚀 **Future Improvements**
-
-- [ ] Fine-tuning on additional datasets
-- [ ] Optimization for mobile deployment
-- [ ] Multi-ethnic performance enhancement
-- [ ] Real-time inference optimization
-
-## 📝 **Citation**
-
-```bibtex
-@misc{vit-age-gender-elite-2025,
-  title={ViT-Age-Gender-Elite: World-Class Facial Analysis with Vision Transformers},
-  author={Abhilash Sahoo},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/abhilash88/ViT-Age-Gender-Elite}
-}
-```
-
-## 🤝 **Contributing**
-
-This model represents cutting-edge research in facial analysis. Contributions and feedback are welcome!
-
-## ⚖️ **Ethics & Bias Considerations**
-
-- Model trained on diverse demographic data
-- Regular bias testing recommended
-- Use responsibly in accordance with privacy laws
-- Not recommended for critical decision-making without human oversight
+## 🚀 **Interactive Demo**
+Try the model instantly: [Hugging Face Space Demo](https://huggingface.co/spaces/abhilash88/ViT-Age-Gender-Elite-Demo)
 
 ---
-
-**Developed by**: Abhilash Sahoo  
-**License**: Apache 2.0  
-**Model Type**: Multi-task Vision Transformer  
-**Performance Tier**: 🏆 ELITE (94.3% accuracy)
+*Updated with actual trained weights | Ready for production use*