MeghanaVP/car-subtype-classifier

🚗 Car Classification Model (196 Classes)

📌 Overview

This project implements a deep learning model for fine-grained car classification across 196 categories using TensorFlow/Keras and an EfficientNetB3 backbone.

The model uses custom preprocessing, advanced pooling (GeM), and strong data augmentation to improve performance.

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🧠 Model Architecture

• Backbone: EfficientNetB3
• Input Shape: (224, 224, 3)
• Output: 196 classes (softmax)
• Total Parameters: 11,755,251

Key Components:

• Custom Layers:

  • CastToFloat32
  • EfficientNetPreprocess
  • GeMPooling

• Data Augmentation:

  • Random Flip
  • Rotation
  • Zoom
  • Brightness & Contrast adjustment
  • Translation

• Fully Connected Head:

  • Dense → BatchNorm → ReLU → Dropout
  • Final Softmax layer

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📊 Performance

• Accuracy: 80%

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📂 Model Details

• Weights file: final_cars.keras
• Model is reconstructed programmatically and weights are loaded separately to avoid serialization issues with Lambda layers.

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⚙️ Usage

1. Load Model

from tensorflow.keras.models import load_model

# Use the provided architecture code
model.load_weights("final_cars.keras")

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2. Inference Example

import numpy as np
from tensorflow.keras.preprocessing import image

img = image.load_img("test.jpg", target_size=(224,224))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)

predictions = model.predict(img)
print(predictions)

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🏗️ How It Works

1. Input image is augmented
2. Converted to float32
3. Preprocessed using EfficientNet preprocessing
4. Passed through EfficientNetB3 backbone
5. Features pooled using GeM pooling
6. Fully connected layers perform classification

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💡 Highlights

• Avoids Lambda layer serialization issues using custom registered layers
• Uses GeM pooling instead of traditional average pooling
• Strong augmentation pipeline improves generalization

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📌 Notes

• Ensure TensorFlow version compatibility when loading weights
• Model expects input images resized to 224×224

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👤 Author

Meghana Poojary