https://github.com/srepasup/cat-and-dog-image-classifier

A convolutional neural network (CNN) built with TensorFlow and Keras to classify images of cats and dogs with over 63% accuracy.
https://github.com/srepasup/cat-and-dog-image-classifier

cnn deep-learning image-classification keras machine-learning tensorflow

Last synced: 2 months ago
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A convolutional neural network (CNN) built with TensorFlow and Keras to classify images of cats and dogs with over 63% accuracy.

• Host: GitHub
• URL: https://github.com/srepasup/cat-and-dog-image-classifier
• Owner: srepasup
• Created: 2025-05-06T03:26:55.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-08T02:36:58.000Z (about 1 year ago)
• Last Synced: 2025-05-08T02:51:54.687Z (about 1 year ago)
• Topics: cnn, deep-learning, image-classification, keras, machine-learning, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.85 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🐶🐱 Cat vs Dog Image Classifier 

![Model Accuracy](https://img.shields.io/badge/accuracy-70%25-brightgreen)



This project uses a Convolutional Neural Network(CNN) built with Keras and TensorFlow 2.0 to classify images as either a cat or a dog. It uses a dataset of 3,000+ labeled images and achieves over 70% accuracy.

✅ Trained on 2,000 images  

✅ Validated on 1,000 images  

✅ Tested on 50 unlabeled images  

## 🎯 Objective

Train a CNN model that can classify cat and dog images with over 63% accuracy.

## 📊 Dataset

The dataset is provided by freeCodeCamp and includes:

- 2,000 training images

- 1,000 validation images

- 50 test images (unlabeled)

The folder structure:

cats_and_dogs/

├── train/

│ ├── cats/

│ └── dogs/

├── validation/

│ ├── cats/

│ └── dogs/

└── test/

└── [unlabeled images]

## 🧪 Model Summary

- Preprocessing with `ImageDataGenerator` (rescaling + augmentation)

- CNN with Conv2D, MaxPooling2D, and Dense layers

- Trained for 15 epochs with a batch size of 128

## 📈 Results

- Achieved over **63% accuracy**

- Successfully passed the automated test

- Visualized predictions on test data

## 🛠 Tools Used

- Python 3.x

- TensorFlow 2.x

- Keras

- Google Colab

- Matplotlib & NumPy

## 🚀 How to Run

1. Open the `.ipynb` notebook in Google Colab.

2. Run all cells in order.

3. Make sure the dataset downloads and unzips correctly.

4. After training, evaluate and visualize predictions in Cells 10 & 11.

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