fastai style transfer

Introduction

The repository provides a trained model for style transfer using a VGG16 backbone. This model enables the transformation of images by applying the style of one image onto another, producing visually appealing results. The project is credited to Nhu Hoang.

Architecture

The model utilizes VGG16, a convolutional neural network known for its depth and performance, as the backbone for style transfer. This architecture is well-suited for extracting and applying stylistic features from one image to another.

Training

During training, the following hyperparameters were used:

• Optimizer: Adam
• Learning Rate: 3e-5
• Training Precision: Float16

These settings were selected to optimize the model's performance in transforming images effectively.

Guide: Running Locally

To run the style transfer model locally, follow these steps:

1. Clone the Repository: Download the model files from the Hugging Face model card.
2. Set Up Environment: Ensure that you have the necessary libraries, such as fastai and PyTorch, installed in your Python environment.
3. Load the Model: Use the provided scripts or guidelines to load the model with the pre-trained weights.
4. Run Inference: Apply the model to your images to perform style transfer.

For optimal performance, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure, to handle the computational demands of the model.

License

The license details for the use and distribution of this model are not specified in the provided information. Please refer to the original repository or contact the author for licensing terms.