DEEPDANBOORU

Introduction

DEEPDANBOORU is a model designed for the classification of anime images. The project is part of the Hugging Face model repository and can be accessed for various tasks related to anime image categorization.

Architecture

The model is built using the ONNX framework, which allows for high-performance evaluation and interoperability with various machine learning tools.

Training

Details on the specific training methodology, dataset, and hyperparameters used for DEEPDANBOORU are not provided in the README. Users interested in training specifics should refer to the GitHub repository for more comprehensive information.

Guide: Running Locally

To run DEEPDANBOORU locally, follow these basic steps:

1. Clone the repository from GitHub.
2. Install the required dependencies listed in the repository.
3. Use the ONNX runtime to load and run the model on your local machine.

For optimized performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure, which provide scalable resources for running machine learning models.

License

DEEPDANBOORU is released under the MIT License, allowing for flexible use and modification, subject to the conditions of the license.