In this liveProject, you’ve taken on the challenge of digitizing the collection of the World Painting Museum, and the head curator wants you to use your machine learning skills to create an index of the art. Your challenges will include classifying your training data, considering pretrained models to help aid categorization, implementing a customized CNN that can identify genre, and applying image semantic segmentation in order to facilitate the classification by identifying the elements present on the art painting.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for intermediate Python programmers with some deep learning experience. To begin this liveProject, you will need to be familiar with:
- Intermediate Python
- Intermediate NumPy
- Basics of PIL
- Basics of Matplotlib
- Basics of PyTorch
- Classification as a machine learning task
- Intermediate deep learning (including convolutional neural networks)
you will learn
In this liveProject, you’ll overcome common problems faced in a deep learning project such as dataset relevance and model accuracy. You’ll master a diverse toolbox of data science and machine learning tools that can be applied to almost any project.
- Building a training dataset of images with ZipFile and the request library
- Elaborating on convolutional neural networks with PyTorch
- Implementing pretrained models
- Image segmentation