Deep Neural Networks for Image Segmentation in Fine Art

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prerequisites
intermediate Python • intermediate NumPy • basics of PyTorch • intermediate deep learning (CNNs)
skills learned
image transformation • build an image classifier-based convolutional neural networks • apply transfer learning for image classification and segmentation • perform image semantic segmentation to increase accuracy of art paintings identification
David Stork and Max Dehaut
5 weeks · 5-7 hours per week · INTERMEDIATE
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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.

prerequisites

This liveProject is for intermediate Python programmers with some deep learning experience. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Intermediate NumPy
  • Basics of PIL
  • Basics of Matplotlib
  • Basics of PyTorch
TECHNIQUES
  • Classification as a machine learning task
  • Intermediate deep learning (including convolutional neural networks)

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