model-optimization and mct-model-optimization
These are complementary tools that address model optimization across different hardware contexts: TensorFlow Model Optimization is a general-purpose framework for Keras/TensorFlow models focusing on quantization and pruning, while MCT specializes in optimization under specific hardware constraints, making them useful together for comprehensive deployment pipelines.
About model-optimization
tensorflow/model-optimization
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
This toolkit helps machine learning engineers and researchers make their trained Keras and TensorFlow models smaller and faster. It takes an existing, functional machine learning model and applies optimization techniques like quantization or pruning. The output is a more efficient model that performs similarly but requires less computational power and memory, ideal for deploying on devices with limited resources.
About mct-model-optimization
SonySemiconductorSolutions/mct-model-optimization
Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.
Deploying neural networks on devices with limited computational power can be challenging. This tool helps optimize your pre-trained PyTorch or Keras models by reducing their size and computational demands, making them efficient for edge devices. It takes your existing floating-point model and outputs a compressed, quantized model suitable for deployment, benefiting AI/ML engineers and researchers working with resource-constrained hardware.
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