yolos fashionpedia

Introduction

YOLOS-FASHIONPEDIA is a fine-tuned object detection model designed for fashion-related applications. It is based on the YOLOS architecture and trained using the Fashionpedia dataset. The model is capable of detecting various fashion items and accessories.

Architecture

The model uses the YOLOS architecture, which is a transformer-based model optimized for object detection tasks. It leverages PyTorch for its implementation and is part of the Hugging Face Transformers library.

Training

YOLOS-FASHIONPEDIA is trained on the Fashionpedia dataset, which includes annotations for a wide array of fashion items. The dataset provides a comprehensive set of categories, making the model suitable for detailed fashion object detection. The categories include clothing items like shirts, blouses, and accessories such as glasses and hats.

Guide: Running Locally

To run this model locally, follow these steps:

1. Install Dependencies: Ensure you have Python and PyTorch installed. You can install the Hugging Face Transformers library using pip:

   pip install transformers
   

2. Download the Model: Clone the repository or download the model files from the Hugging Face Model Hub.

3. Setup Environment: Load the model and tokenizer using the Transformers library.

4. Inference: Use the model to perform object detection on your images.

For optimal performance, consider using a cloud GPU service such as AWS, Google Cloud, or Azure.

License

The model and its associated files are provided under a license specified in the repository. Ensure to review the terms for usage and distribution.