Building a Dog vs Cat Classifier Using Deep Learning and Deploying with Flask

Deep learning has revolutionized image classification, and in this blog, I will walk you through building a Dog vs Cat classifier using TensorFlow, Keras, and Neural Networks. Additionally, I will show how to deploy this model using Flask for real-world use.

1. Dataset Preparation

For this project, we use the Kaggle Cats vs Dogs dataset, consisting of thousands of labeled images.

• Data Collection: Obtain the dataset.
• Data Cleaning: Remove noisy or corrupted images.
• Image Preprocessing: Resize images to 128x128 pixels.
• Data Augmentation: Apply transformations like rotation and flipping.
• Splitting Data: Divide into training, validation, and test sets.

2. Building the Deep Learning Model

We use a Convolutional Neural Network (CNN) for classification.

• Convolutional Layers: Extract features.
• Activation Functions: Use ReLU.
• Pooling Layers: Reduce spatial dimensions.
• Fully Connected Layers: Make predictions.
• Dropout Regularization: Prevent overfitting.

3. Training and Evaluation

We train the model by feeding images, computing loss, and updating weights.

• Feed data in batches.
• Calculate loss function.
• Use an optimizer to adjust weights.
• Monitor accuracy on validation data.

4. Saving and Loading the Model

Once trained, the model is saved for deployment.

• Prevents retraining and saves time.
• Ensures reproducibility.
• Facilitates deployment.

5. Deploying with Flask

Flask enables us to create an API where users can upload an image for classification.

• Lightweight and easy to use.
• Supports REST API integration.
• Scalable for cloud deployment.

6. Running and Testing the API

Once the server is running, users can send images through API requests.

• Upload an image.
• Preprocess the image.
• Get classification results in JSON format.

7. Future Enhancements and Optimization

Possible improvements:

• Use Transfer Learning with pre-trained models.
• Optimize hyperparameters.
• Deploy on AWS, GCP, or Azure.
• Build a web interface for user-friendly interaction.

Conclusion

We explored building a Dog vs Cat classifier with deep learning and deploying it using Flask. This project showcases the power of machine learning in real-world applications.

You can check out the full project on my GitHub or view a live demo below:

View Demo