AMA: How to Select Semantic Segmentation Architectures for Production

Semantic segmentation, or image segmentation, clusters parts of an image together that belong to the same object class. Today, this computer vision task is being used in a wide variety of applications including self-driving cars, medical imaging, and virtual fitting rooms.

The process of selecting the right semantic segmentation architecture and deploying it successfully can be tedious and time-consuming. In this AMA with Yonatan Geifman and Lior Kadoch, you will:

  • Hear from the deep learning community some challenges in choosing the optimal segmentation architecture
  • Learn the main considerations and best practices when selecting segmentation models
  • Understand the common issues in deploying segmentation models on edge devices and how to overcome them

To get started with your semantic segmentation project, book a demo of our Deep Learning Development Platform or chat with one of our deep learning experts.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")