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title: AI Pet Classifier
emoji: 🐶
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: "1.46.1"
app_file: app.py
pinned: false
license: mit
---
# 🐶 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.
...
# 🐶🐱 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
│
▼
Image Preprocessing
│
▼
Data Augmentation
│
▼
CNN Model
│
▼
Training
│
▼
Evaluation
│
▼
Prediction
```
---
## CNN Architecture
```
Input Layer (64×64×3)
↓
Conv2D (32 Filters)
↓
Batch Normalization
↓
MaxPooling
↓
Conv2D (64 Filters)
↓
MaxPooling
↓
Conv2D (128 Filters)
↓
MaxPooling
↓
Flatten
↓
Dense (128)
↓
Dense (1, Sigmoid)
↓
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.
---
⭐ If you found this project helpful, consider giving it a star. |