enot-autodl 3.7.2

AutoDL framework for neural network compression & acceleration

Embedded Network Optimization Technology

ENOT, or Embedded Network Optimization Technology, is a flexible tool for Deep Learning developers which automates neural architecture optimization. It can be useful in the following scenarios:

• Target metric maximization (e.g., classification accuracy or intersection over union);
• Target metric maximization with constrained computational resources (e.g., RAM, latency);

Framework advantages:

• Controlled ratio between latency and network performance;
• Networks in the pre-trained search space can exceed their stand-alone variants (in some scenarios);
• Compatibility with almost any DL task and simple integration with the existing training pipelines.
• Joint neural architecture search, prunning and distillation procedure can be applied to found optimal neural network architecture.

To use this package please refer to our documentation page.

Visit our website for more information.