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---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
model_name: microsoft/git-base
pipeline_tag: image-to-text
library_name: transformers
datasets:
- ayoubkirouane/One-Piece-anime-captions
---
# Model Details
+ **Model Name**: Git-base-One-Piece
+ **Base Model**: Microsoft's "git-base" model
+ **Model Type**: Generative Image-to-Text (GIT)
+ **Fine-Tuned** On: 'One-Piece-anime-captions' dataset
+ **Fine-Tuning Purpose**: To generate text captions for images related to the anime series "One Piece."
## Model Description
**Git-base-One-Piece** is a fine-tuned variant of Microsoft's **git-base** model, specifically trained for the task of generating descriptive text captions for images from the **One-Piece-anime-captions** dataset.
The dataset consists of **856 {image: caption}** pairs, providing a substantial and diverse training corpus for the model.
The model is conditioned on both CLIP image tokens and text tokens and employs a **teacher forcing** training approach. It predicts the next text token while considering the context provided by the image and previous text tokens.
## Limitations
+ 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.
## Usage
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-to-text", model="ayoubkirouane/git-base-One-Piece")
```
**or**
```python
# 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")
``` |