Upload README.md

README.md

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+# 🚗 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.
+
+---
+
+## 🧠 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
+
+---
+
+## 📊 Performance
+
+* **Accuracy: 80%**
+
+---
+
+## 📂 Model Details
+
+* Weights file: `final_cars.keras`
+* Model is reconstructed programmatically and **weights are loaded separately** to avoid serialization issues with Lambda layers.
+
+---
+
+## ⚙️ Usage
+
+### 1. Load Model
+
+```python
+from tensorflow.keras.models import load_model
+
+# Use the provided architecture code
+model.load_weights("final_cars.keras")
+```
+
+---
+
+### 2. Inference Example
+
+```python
+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)
+```
+
+---
+
+## 🏗️ 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
+
+---
+
+## 💡 Highlights
+
+* Avoids Lambda layer serialization issues using **custom registered layers**
+* Uses **GeM pooling** instead of traditional average pooling
+* Strong augmentation pipeline improves generalization
+
+---
+
+## 📌 Notes
+
+* Ensure TensorFlow version compatibility when loading weights
+* Model expects input images resized to **224×224**
+
+---
+
+## 👤 Author
+
+Meghana Poojary