Webinar: How to Find the Correct Deep Learning Model for Your Task

Finding the correct deep learning model is important for reducing costs, enabling inference at the edge, and improving the user experience. Unfortunately, in DL, there is no “one-model fits all”. Different use cases, tasks, and hardware call for different architectures.

Beyond the challenge of mastering the techniques for measuring performance, maximizing performance itself is always complex. In this webinar, you’ll learn how to measure and identify the fastest model for specific use cases. You’ll be able to easily cast the insights presented in this webinar to other performance metrics such as memory footprint, energy consumption, and latency.

Watch this 30-minute webinar to learn:

  • What to consider when searching for high-performance models.
    Insights into the structure of architectures optimized for various hardware devices.
  • Examples of high-performance models that were discovered using Deci’s Automated Neural
  • Architecture Construction (AutoNAC) technology.

After viewing the webinar, you can learn more about DeciNets here.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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