https://github.com/muhammadtayyebhamid/cats-vs-dogs

Implemented CNN using tensorflow's cats_vs_dogs dataset.
https://github.com/muhammadtayyebhamid/cats-vs-dogs

cnn-classification neural-networks

Last synced: 27 days ago
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Implemented CNN using tensorflow's cats_vs_dogs dataset.

• Host: GitHub
• URL: https://github.com/muhammadtayyebhamid/cats-vs-dogs
• Owner: MuhammadTayyebHamid
• Created: 2024-08-14T17:04:01.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-14T17:25:17.000Z (5 months ago)
• Last Synced: 2024-08-14T18:31:23.804Z (5 months ago)
• Topics: cnn-classification, neural-networks
• Language: Jupyter Notebook
• Homepage:
• Size: 10.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Cats-vs-Dogs Classification Using Tensorflow and Keras

This project demonstrates how to classify images of cats and dogs using the TensorFlow and Keras libraries. The Cats vs Dogs dataset from TensorFlow was used for training, validation, and testing of the model.

## Dataset

The dataset used in this project is the Cats vs Dogs dataset from TensorFlow. The dataset was divided into three parts:

    Training set: 70% of the total dataset

    Validation set: 20% of the total dataset

    Test set: 10% of the total dataset

### Citation

    @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,

    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},

    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},

    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},

    year = {2007},

    month = {October},

    publisher = {Association for Computing Machinery, Inc.},

    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},

    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},

    }

## Data Preprocessing

The data was pre-processed by resizing the images to 128 x 128, followed by normalizing the images.

## Model Architecture

The model was built using a Convolutional Neural Network (CNN) architecture with TensorFlow and Keras. The architecture includes several convolutional layers followed by max-pooling layers, and fully connected layers leading to the output.

## Results

### Training Set

    Accuracy: 0.9171

    Loss: 0.19

### Validation Set

    Accuracy: 0.8556

    Loss: 0.32

### Testing Accuracy & Loss

    Accuracy: 0.856

    Loss: 0.40