Fine-tuning PaliGemma for Image Captioning

This project demonstrates how to fine-tune PaliGemma model for image captioning. The PaliGemma model, developed by Google Research, is designed to handle images and generate corresponding captions.

Overview

The notebook includes the following key steps:

1. Setup Environment:

• Environment variables for Kaggle are set up to download model checkpoints.
• The Big Vision repository is cloned, and necessary dependencies are installed.
• JAX is configured, and GPU/TPU devices are made invisible to TensorFlow to avoid conflicts.

2. Download Required Files:

• Model checkpoint and tokenizer are downloaded.
• The dataset for training and validation is downloaded.

3. Model Definition:

• The PaliGemma model is defined using configuration settings.
• The tokenizer for text processing is loaded.

4. Parameter Loading:

• Model parameters are loaded and sharded across devices if multiple GPUs are available.
• The parameters are converted to float32 where necessary.

5. Preprocessing Functions:

• Functions for preprocessing images and tokens are defined to prepare data for the model.

6. Training and Validation Data:

• Iterators for training and validation data are created.
• Images and captions from the dataset are preprocessed for training and evaluation.

7. Training Loop:

A short training loop is implemented using stochastic gradient descent (SGD) and a cosine learning rate schedule. Model parameters are updated, and training loss is reported at each step.

8. Evaluation:

• The model's performance is evaluated on a validation dataset.
• Predicted captions for validation images are generated and displayed.

9. Streamlit Interface:

• A Streamlit interface is created to upload images and generate captions using the trained PALIGEMMA model.

How to Use

1. Clone the Repository:

• Clone the Big Vision repository and install dependencies.

2. Download Model and Data:

• Download the PALIGEMMA model checkpoint, tokenizer, and dataset.

3. Run the Notebook:

• Execute the notebook to preprocess data, load the model, and perform training and evaluation.

4. Streamlit Interface:

• Run the Streamlit interface to upload an image and generate a caption.

Conclusion

This project provides a comprehensive guide to fine-tune the PaliGemma model for image captioning. By following the steps outlined in the notebook, you can train the model on a custom dataset and use it to generate captions for new images.

License

This project is licensed under the MIT License. See the LICENSE file for details.