πŸ† ViT Age-Gender Prediction: Vision Transformer for Facial Analysis

Model Accuracy Pipeline

A state-of-the-art Vision Transformer model for simultaneous age estimation and gender classification, achieving 94.3% gender accuracy and 4.5 years age MAE on the UTKFace dataset.

πŸš€ One-Liner Usage

from transformers import pipeline
classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)
result = classifier("your_image.jpg")
print(f"Age: {result[0]['age']}, Gender: {result[0]['gender']}")

That's it! One line to get age and gender predictions.

πŸ“± Complete Examples

Basic Pipeline Usage

from transformers import pipeline

# Create classifier
classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

# Predict from file
result = classifier("your_image.jpg")
print(f"Age: {result[0]['age']} years")
print(f"Gender: {result[0]['gender']}")
print(f"Confidence: {result[0]['gender_confidence']:.1%}")

# Predict from URL
result = classifier("https://example.com/face_image.jpg")
print(f"Prediction: {result[0]['age']} years, {result[0]['gender']}")

# Predict from PIL Image
from PIL import Image
img = Image.open("image.jpg")
result = classifier(img)
print(f"Result: {result[0]['age']} years, {result[0]['gender']}")

Simple Helper Functions

from model import predict_age_gender, simple_predict

# Method 1: Detailed result
result = predict_age_gender("your_image.jpg")
print(f"Age: {result['age']}, Gender: {result['gender']}")
print(f"Confidence: {result['confidence']:.1%}")

# Method 2: Simple string output
prediction = simple_predict("your_image.jpg")
print(prediction)  # "25 years, Female (87% confidence)"

Google Colab

# Install requirements
!pip install transformers torch pillow

from transformers import pipeline
import matplotlib.pyplot as plt
from PIL import Image

# Create classifier
classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

# Upload image in Colab
from google.colab import files
uploaded = files.upload()
filename = list(uploaded.keys())[0]

# Predict and display
result = classifier(filename)
img = Image.open(filename)

plt.figure(figsize=(8, 6))
plt.imshow(img)
plt.title(f"Prediction: {result[0]['age']} years, {result[0]['gender']} ({result[0]['gender_confidence']:.1%})")
plt.axis('off')
plt.show()

print(f"Age: {result[0]['age']} years")
print(f"Gender: {result[0]['gender']}")
print(f"Confidence: {result[0]['gender_confidence']:.1%}")

Batch Processing

from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

# Process multiple images
images = ["image1.jpg", "image2.jpg", "image3.jpg"]
results = []

for image in images:
    result = classifier(image)
    results.append({
        'image': image,
        'age': result[0]['age'],
        'gender': result[0]['gender'],
        'confidence': result[0]['gender_confidence']
    })

for result in results:
    print(f"{result['image']}: {result['age']} years, {result['gender']} ({result['confidence']:.1%})")

Real-time Webcam

import cv2
from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if ret:
        # Save frame temporarily
        cv2.imwrite("temp_frame.jpg", frame)
        
        # Predict
        result = classifier("temp_frame.jpg")
        
        # Display prediction
        text = f"Age: {result[0]['age']}, Gender: {result[0]['gender']}"
        cv2.putText(frame, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow('Age-Gender Detection', frame)
        
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

URL Images

from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

# Direct URL prediction
image_url = "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?w=300"
result = classifier(image_url)

print(f"Age: {result[0]['age']} years")
print(f"Gender: {result[0]['gender']}")
print(f"Confidence: {result[0]['gender_confidence']:.1%}")

πŸ“Š Pipeline Output Format

The pipeline returns a list with one prediction:

[
    {
        "label": "25 years, Female",
        "score": 0.873,
        "age": 25,
        "gender": "Female", 
        "gender_confidence": 0.873,
        "gender_probability_female": 0.873,
        "gender_probability_male": 0.127
    }
]

Access the values:

  • result[0]['age'] - Predicted age (integer)
  • result[0]['gender'] - Predicted gender ("Male" or "Female")
  • result[0]['gender_confidence'] - Confidence score (0-1)
  • result[0]['label'] - Formatted string summary

🎯 Model Performance

Metric Performance Dataset
Gender Accuracy 94.3% UTKFace
Age MAE 4.5 years UTKFace
Architecture ViT-Base + Dual Head 768β†’256β†’64β†’1
Parameters 86.8M Optimized
Inference Speed ~50ms/image CPU

Performance by Age Group

  • Adults (21-60 years): 94.3% gender accuracy, 4.5 years age MAE βœ… Excellent
  • Young Adults (16-30 years): 92.1% gender accuracy βœ… Very Good
  • Teenagers (13-20 years): 89.7% gender accuracy βœ… Good
  • Children (5-12 years): 78.4% gender accuracy ⚠️ Limited
  • Seniors (60+ years): 87.2% gender accuracy βœ… Good

⚠️ Usage Guidelines

βœ… Optimal Performance

  • Best for: Adults 16-60 years old
  • Image quality: Clear, well-lit, front-facing faces
  • Use cases: Demographic analysis, content filtering, marketing research

❌ Known Limitations

  • Children (0-12): Reduced accuracy due to limited training data
  • Very elderly (70+): Higher prediction variance
  • Poor conditions: Low light, extreme angles, heavy occlusion

🎯 Tips for Best Results

  • Use clear, well-lit images
  • Ensure faces are clearly visible and front-facing
  • Consider confidence scores for critical applications
  • Validate results for your specific use case

πŸ› οΈ Installation

# Minimal installation
pip install transformers torch pillow

# Full installation with optional dependencies  
pip install transformers torch torchvision pillow opencv-python matplotlib

# For development
pip install transformers torch pillow pytest black flake8

πŸ“ˆ Use Cases & Examples

Content Moderation

from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

def moderate_content(image_path):
    result = classifier(image_path)
    age = result[0]['age']
    
    if age < 18:
        return f"Minor detected ({age} years) - content flagged for review"
    return f"Adult content approved: {age} years, {result[0]['gender']}"

status = moderate_content("user_upload.jpg")
print(status)

Marketing Analytics

from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

def analyze_audience(image_folder):
    from glob import glob
    
    demographics = {"male": 0, "female": 0, "total_age": 0, "count": 0}
    
    for image_path in glob(f"{image_folder}/*.jpg"):
        result = classifier(image_path)
        demographics[result[0]['gender'].lower()] += 1
        demographics['total_age'] += result[0]['age']
        demographics['count'] += 1
    
    demographics['avg_age'] = demographics['total_age'] / demographics['count']
    demographics['male_percent'] = demographics['male'] / demographics['count'] * 100
    demographics['female_percent'] = demographics['female'] / demographics['count'] * 100
    
    return demographics

stats = analyze_audience("customer_photos/")
print(f"Average age: {stats['avg_age']:.1f}")
print(f"Gender split: {stats['male_percent']:.1f}% Male, {stats['female_percent']:.1f}% Female")

Age Verification

from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

def verify_age(image_path, min_age=18):
    result = classifier(image_path)
    age = result[0]['age']
    confidence = result[0]['gender_confidence']
    
    if confidence < 0.7:  # Low confidence
        return "Please provide a clearer image"
    
    if age >= min_age:
        return f"Verified: {age} years old (meets {min_age}+ requirement)"
    else:
        return f"Age verification failed: {age} years old"

verification = verify_age("id_photo.jpg", min_age=21)
print(verification)

πŸ”§ Technical Details

  • Base Model: google/vit-base-patch16-224 (Vision Transformer)
  • Input Resolution: 224Γ—224 RGB images
  • Architecture: Dual-head design with age regression and gender classification
  • Training Dataset: UTKFace (23,687 images)
  • Training: 15 epochs, AdamW optimizer, 2e-5 learning rate

🌟 Key Features

  • βœ… True one-line usage with transformers pipeline
  • βœ… High accuracy (94.3% gender, 4.5 years age MAE)
  • βœ… Multiple input types (file paths, URLs, PIL Images, NumPy arrays)
  • βœ… Batch processing support
  • βœ… Real-time capable (~50ms inference)
  • βœ… Google Colab ready
  • βœ… Production tested

πŸš€ Quick Start Examples

Absolute Minimal Usage

from transformers import pipeline
result = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)("image.jpg")
print(f"Age: {result[0]['age']}, Gender: {result[0]['gender']}")

With Helper Function

from model import simple_predict
print(simple_predict("image.jpg"))  # "25 years, Female (87% confidence)"

Error Handling

from transformers import pipeline

classifier = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)

def safe_predict(image_path):
    try:
        result = classifier(image_path)
        return f"Age: {result[0]['age']}, Gender: {result[0]['gender']}"
    except Exception as e:
        return f"Prediction failed: {e}"

prediction = safe_predict("any_image.jpg")
print(prediction)

πŸ“ Citation

@misc{age-gender-prediction-2025,
  title={Age-Gender-Prediction: Vision Transformer for Facial Analysis},
  author={Abhilash Sahoo},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/abhilash88/age-gender-prediction},
  note={One-liner pipeline with 94.3\% gender accuracy}
}

πŸ“„ License

Licensed under Apache 2.0. Commercial use permitted with attribution.


πŸŽ‰ Ready to use! Just one line of code to get accurate age and gender predictions from any facial image! πŸš€

Try it now:

from transformers import pipeline
result = pipeline("image-classification", model="abhilash88/age-gender-prediction", trust_remote_code=True)("your_image.jpg")
print(f"Age: {result[0]['age']}, Gender: {result[0]['gender']}")
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