White Paper

Supercharging One of the World’s Fastest Segmentation Models by 3X

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Semantic segmentation is an algorithm that is key to applications such as autonomous driving, bio-medical diagnosis, and other image-intensive implementations. The problem is that semantic segmentation is so computationally expensive that it has become a barrier to commercialization.

In this white paper you will learn about:

  • Why semantic segmentation models are crucial for real-world applications
  • What is holding semantic segmentation back (hint: accuracy, complexity, latency and throughput)
  • A case study about how Deci’s AutoNAC was able to take the best segmentation architecture (ShelfNet) and catapult its performance 3X, reducing its memory footprint and enabling more efficient utilization of the GPU.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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