Bob Ross Painting Identifier: Computer Vision and Machine Learning

toldham2
Jul 12, 2023
2 min read

Creating a transfer learning TensorFlow model to identify the artist of a painting.

In our increasingly digitized world, the lines between art, technology, and machine learning are becoming blurred. As an illustration of this intersection, let me introduce you to a fascinating project I've been working on that integrates machine learning into art identification: "Is it Bob Ross?"

The idea behind my project is simple yet ambitious: to use machine learning to identify the artist of a given artwork, with a focus on the beloved American painter Bob Ross. I set a lofty goal of achieving 90% accuracy in my model’s predictions.

Training the Machine-Learning Model

I used a relatively small dataset of 5000 images to train the model. This dataset included many artists to ensure a comprehensive understanding and recognition capability. Among the 5000 images, 500 were paintings by Bob Ross, the star of my project. To add to the complexity and to make the model more robust, I implemented data augmentation and dropout, common techniques in machine learning to prevent overfitting and improve the model's generalization.

Selecting an Effective Pre-trained Model

An integral part of this project was finding an effective pre-trained model. Pretrained models are beneficial as they have already been trained on large datasets and learned to identify complex patterns and features. This allows for more accurate predictions without requiring massive amounts of data, which might not always be available or feasible to collect.

Testing the Model

Once the model was trained, I set up a system where I could upload images from the web to be tested. This makes the project interactive and allows users to see the model's capabilities in real-time. This tool only predicts whether a painting is or isn't by Bob Ross but acts as a proof of concept for a more comprehensive model.

Results

The final results were impressive. In a test, my model correctly identified the artists of five artworks, with an average confidence of 85%. These artists included Bob Ross, Claude Monet, and Pierre-Auguste Renoir— artists with similar subjects and styles— showcasing the model's versatility and breadth of knowledge.

The project "Is it Bob Ross?" is an excellent example of how machine learning can be utilized in unconventional areas such as art identification. While it was designed with Bob Ross in mind, the possibilities for applying this technology are vast. They can range from identifying other artists to potentially uncovering unknown works of famous painters. This project is a testament to the amazing potential at the intersection of art and technology.

As technology evolves and machine learning becomes even more sophisticated, who knows? Maybe one day, I'll have a tool to identify every artist in the world just by glancing at their artwork. For now, we'll have to be content with asking, "Is it Bob Ross?"