https://github.com/tameronline/classification-with-transfer-learning

Image classification using transfer learning with VGG16 on the CIFAR-10 dataset, implemented with TensorFlow and Keras.
https://github.com/tameronline/classification-with-transfer-learning

ai classification-internal deep-learning machine-learning python

Last synced: about 8 hours ago
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Image classification using transfer learning with VGG16 on the CIFAR-10 dataset, implemented with TensorFlow and Keras.

• Host: GitHub
• URL: https://github.com/tameronline/classification-with-transfer-learning
• Owner: TamerOnLine
• License: other
• Created: 2024-10-16T23:35:27.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-10-16T23:43:21.000Z (about 1 month ago)
• Last Synced: 2024-10-18T23:57:24.293Z (28 days ago)
• Topics: ai, classification-internal, deep-learning, machine-learning, python
• Language: Jupyter Notebook
• Homepage: https://tameronline.github.io/Classification-with-Transfer-Learning/
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Security: SECURITY.md

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README

        

# Image Classification Using Transfer Learning

This project demonstrates image classification using a pre-trained model (VGG16) through transfer learning. The CIFAR-10 dataset is used, which consists of 60,000 32x32 color images in 10 different classes.

## Overview

In this project, we leverage the power of transfer learning by using the VGG16 model, pre-trained on the ImageNet dataset, to classify images from the CIFAR-10 dataset. The final layers of the model are fine-tuned to fit our classification task.

## Dataset

The CIFAR-10 dataset is used in this project. It includes the following:

- 50,000 training images

- 10,000 test images

- 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck

## Model Architecture

- **Base Model**: VGG16 pre-trained on ImageNet

- **Fine-tuned Layers**: The last layers of VGG16 are replaced with fully connected layers tailored for the CIFAR-10 classification task.

- **Optimizer**: Adam

- **Loss Function**: Categorical Crossentropy

## Data Preprocessing

- Resizing images to 32x32 pixels to fit the model's input requirements.

- One-hot encoding of the labels.

- Data augmentation using rotation, zoom, shift, and flip techniques to prevent overfitting.

## Training

- **Batch size**: 64

- **Epochs**: 25

- **Callbacks**: Early stopping and model checkpointing are used to save the best model and avoid overfitting.

- **Data Augmentation**: Applied to enhance the model's ability to generalize.

## Results

The model achieved the following performance metrics:

- **Training Accuracy**: `XX%`

- **Validation Accuracy**: `XX%`

- **Training Loss**: `XX`

- **Validation Loss**: `XX`

## Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/YourUsername/YourRepoName.git

   ```

2. Install the dependencies:

   ```bash

   pip install -r requirements.txt

   ```

## Usage

To run the model training, use the following command:

```bash

python train_model.py

```

You can find the saved model and training logs in the `models/` directory.

## Conclusion

This project demonstrates how transfer learning can be effectively used for image classification tasks. By leveraging pre-trained models, we achieve high accuracy with less computational power and time.

## License

This project is licensed under the MIT License.

## Acknowledgments

- The [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html)

- [TensorFlow](https://www.tensorflow.org/)

- [Keras](https://keras.io/)