README.md

(WIP) MakeItSports Bot Image-to-Art Search

This project fine-tunes a Vision Transformer (ViT) model, pre-trained with "google/vit-base-patch32-224-in21k" weights and fine tuned with the style of ArtButMakeItSports, to perform image-to-art search across 81k artworks made available by WikiArt.

Table of Contents

• Overview
• Installation
• Usage
• Dataset
• Training
• Inference
• Contributing
• License

Overview

This project leverages the Vision Transformer (ViT) model architecture for the task of image-to-art search. By fine-tuning the pre-trained ViT model on a custom dataset derived from the Instagram account ArtButMakeItSports, we aim to create a model capable of matching images (but not only) to corresponding artworks, being able to search for any of the images on WikiArt.

Installation

1. Clone the repository:

   git clone https://github.com/brunorosilva/makeitsports-bot.git
   cd makeitsports-bot
   

2. Install poetry:

   pip install poetry
   

3. Install using poetry:

   poetry install
   

How it works

Dataset Preparation

1. Download images from the ArtButMakeItSports Instagram account.
2. Organize the images into appropriate directories for training and validation.

Training

1. Fine-tune the ViT model:

   poetry run python main.py train --epochs 50 --batch_size 32
   

Inference via Gradio

1. Perform image-to-art search using the fine-tuned model:

   poetry run python main.py interface
   

Create new gallery

1. If you want to index new images to search, use:

   poetry run python main.py gallery --gallery_path <your_path>
   

Dataset

The dataset derives from 1k images from the Instagram account ArtButMakeItSports. Images are downloaded and split into training, validation and test sets. Each image is paired with its corresponding artwork for training purposes, if you want this dataset just ask me stating your usage.

WikiArt is indexed using the same process, except that there's no expected result. So each artwork is mapped to itself and the embeddings are saved as a numpy file (will be changed to chromadb in the future).

Training

The training script fine-tunes the ViT model on the prepared dataset. Key steps include:

1. Loading the pre-trained "google/vit-base-patch32-224-in21k" weights.
2. Preparing the dataset and data loaders.
3. Fine-tuning the model using a custom training loop.