🐶 AI Pet Classifier using CNN
A deep learning application that classifies images of cats and dogs using a Convolutional Neural Network (CNN) built with TensorFlow and Keras.
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🐶🐱 AI Pet Classifier using Convolutional Neural Networks (CNN)
Overview
AI Pet Classifier is a deep learning project that classifies images as either Cat or Dog using a Convolutional Neural Network (CNN) built with TensorFlow and Keras. The model is trained on thousands of labeled pet images and predicts the class of unseen images with high accuracy.
Features
- Binary Image Classification
- TensorFlow & Keras Implementation
- Data Augmentation
- Batch Normalization
- Image Preprocessing
- Model Saving & Loading
- Single Image Prediction
- Beginner-Friendly Notebook
- Google Colab Compatible
Project Pipeline
Dataset
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Image Preprocessing
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Data Augmentation
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CNN Model
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Training
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Evaluation
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Prediction
CNN Architecture
Input Layer (64×64×3)
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Conv2D (32 Filters)
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Batch Normalization
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MaxPooling
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Conv2D (64 Filters)
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MaxPooling
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Conv2D (128 Filters)
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MaxPooling
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Flatten
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Dense (128)
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Dense (1, Sigmoid)
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Prediction
Technologies Used
- Python
- TensorFlow
- Keras
- NumPy
- Matplotlib
- Pillow
- Google Colab
Dataset Structure
Data/
├── training_set/
│ ├── cats/
│ └── dogs/
│
└── test_set/
├── cats/
└── dogs/
Hyperparameters
| Parameter | Value |
|---|---|
| Image Size | 64 × 64 |
| Batch Size | 32 |
| Epochs | 25 |
| Optimizer | Adam |
| Loss Function | Binary Crossentropy |
| Activation | ReLU |
| Output Activation | Sigmoid |
Training
The model uses image augmentation to improve generalization by applying:
- Rescaling
- Random Zoom
- Shear Transformation
- Horizontal Flip
Prediction
The trained model predicts whether the uploaded image belongs to:
- 🐱 Cat
- 🐶 Dog
along with the prediction confidence.
Future Improvements
- Early Stopping
- Model Checkpoint
- Transfer Learning (MobileNetV2 / EfficientNet)
- Confusion Matrix
- Classification Report
- Accuracy & Loss Curves
- Grad-CAM Visualization
- Streamlit & Hugging Face Deployment
Repository Structure
├── CNN_Model.ipynb
├── cnn_model.keras
├── Data.zip
├── requirements.txt
├── README.md
└── LICENSE
Author
Sudheer Muthyala
B.Tech (ECE)
Aspiring AI & Data Science Engineer
GitHub: https://github.com/M-Sudheer18
License
This project is licensed under the MIT License.
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