---
license: mit
tags:
  - image-classification
  - keras
  - cnn
  - vehicles
  - car-vs-bike
library_name: keras
model-index:
  - name: Car vs Bike Classifier
    results:
      - task:
          type: image-classification
          name: Image Classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.96
---

# 🚗🏍️ Car vs Bike Image Classifier

This repository contains a Convolutional Neural Network (CNN) model trained to classify whether an image contains a **car** or a **bike**.

## 📊 Model Overview

- **Input:** RGB image resized to 128x128
- **Output:** Binary classification (0 = Bike, 1 = Car)
- **Framework:** TensorFlow / Keras
- **Model Format:** `.h5` (HDF5)

## 🧠 Architecture Summary

- Conv2D → ReLU → MaxPooling
- Conv2D → ReLU → MaxPooling
- Flatten → Dense(128) → Dropout
- Dense(1) with sigmoid activation

## ✅ Performance

- Accuracy: ~96% on validation data
- Loss: ~0.12 (binary crossentropy)

## 🧪 Example Usage (Python)

```python
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np

# Load model
model = load_model("car_vs_bike_model.h5")

# Load and preprocess image
img = image.load_img("test.jpg", target_size=(128, 128))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

# Predict
prediction = model.predict(img_array)

if prediction > 0.5:
    print("Prediction: 🚗 Car")
else:
    print("Prediction: 🏍️ Bike")
```

## 📁 Files in This Repo

- `car_vs_bike_model.h5` → Trained model file
- `README.md` → Project description & usage
- (Optional) `example_images/` → Sample test images

## 📥 Download Instructions

Clone this repo and use Git LFS for large model files:

```bash
git lfs install
git clone https://huggingface.co/D-Neeraja/car_vs_bike_classifier
```

## 🧰 Requirements

- Python 3.7+
- TensorFlow ≥ 2.5
- NumPy

## 🙋‍♀️ Author

**Neeraja Dakkata**  
📧 dakkataneeraja0031@gmail.com  
🔗 GitHub: [github.com/D-Neeraja](https://github.com/D-Neeraja)

---

*Feel free to use this model for educational purposes or integrate it into your own projects.*