Git Base One Piece

🚀 Git-base-One-Piece Model

A fine - tuned model for generating text captions for "One Piece" anime images

🚀 Quick Start

The Git-base-One-Piece model is designed to generate descriptive text captions for images from the "One Piece" anime series. You can use it in the following ways:

💻 Usage Examples

Basic Usage

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-to-text", model="ayoubkirouane/git-base-One-Piece")

Advanced Usage

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("ayoubkirouane/git-base-One-Piece")
model = AutoModelForCausalLM.from_pretrained("ayoubkirouane/git-base-One-Piece")

✨ Features

• Fine - Tuned for One Piece: Specifically trained on the "One-Piece-anime-captions" dataset to generate relevant captions for "One Piece" anime images.
• Generative Image - to - Text: Based on the Generative Image-to-Text (GIT) model type, capable of generating text from images.

📦 Installation

To use this model, you need to have the transformers library installed. You can install it using the following command:

pip install transformers

📚 Documentation

Model Details

Model Description

Git-base-One-Piece is a fine-tuned variant of Microsoft's git-base model. It is specifically trained for generating descriptive text captions for images from the One-Piece-anime-captions dataset.

The dataset contains 856 {image: caption} pairs, which offers a substantial and diverse training corpus for the model.

The model is conditioned on both CLIP image tokens and text tokens and uses a teacher forcing training approach. It predicts the next text token while considering the context provided by the image and previous text tokens.

Limitations

⚠️ Important Note

• The quality of generated captions may vary depending on the complexity and diversity of images from the One-Piece-anime-captions dataset.
• The model's output is based on the data it was fine-tuned on, so it may not generalize well to images outside the dataset's domain.
• Generating highly detailed or contextually accurate captions may still be a challenge.

📄 License

This project is licensed under the MIT License.