Classification-with-Transfer-Learning

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

Clone the repository:

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

Install the dependencies:
```
pip install -r requirements.txt
```

Usage

To run the model training, use the following command:

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

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