PocketFlow - An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications

•    Python

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PocketFlow is an open-source framework for compressing and accelerating deep learning models with minimal human effort. Deep learning is widely used in various areas, such as computer vision, speech recognition, and natural language translation. However, deep learning models are often computational expensive, which limits further applications on mobile devices with limited computational resources. PocketFlow aims at providing an easy-to-use toolkit for developers to improve the inference efficiency with little or no performance degradation. Developers only needs to specify the desired compression and/or acceleration ratios and then PocketFlow will automatically choose proper hyper-parameters to generate a highly efficient compressed model for deployment.

deep-learning model-compression mobile-app automl computer-vision

NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

•    Python

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NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: Block Zoo and Model Zoo.

question-answering deep-learning pytorch natural-language-processing text-classification artificial-intelligence dnn qna text-matching knowledge-distillation model-compression

PaddleSlim - PaddleSlim is an open-source library for deep model compression and architecture search

•    Python

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PaddleSlim is an open-source library for deep model compression and architecture search.

pruning quantization nas knowledge-distillation model-compression neural-architecture-search hyperparameter-search autodl

model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning

•    Python

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The TensorFlow Model Optimization Toolkit is a suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution. Supported techniques include quantization and pruning for sparse weights. There are APIs built specifically for Keras.

machine-learning sparsity compression deep-learning tensorflow optimization keras ml pruning quantization model-compression quantized-training quantized-neural-networks quantized-networks

channel-pruning - Channel Pruning for Accelerating Very Deep Neural Networks

•    Python

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Please have a look at AMC: AutoML for Model Compression and Acceleration on Mobile Devices ECCV'18, which combines channel pruning and reinforcement learning to further accelerate CNN.

image-recognition model-compression acceleration object-detection image-classification channel-pruning deep-neural-networks

mobile-id - Deep Face Model Compression

•    Matlab

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Further information please contact Ziwei Liu. Note that there are no identity overlapping between CelebA Dataset and LFW Dataset.

computer-vision deep-learning face-recognition model-compression efficient-inference

condensa - Programmable Neural Network Compression

•    Python

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Condensa is a framework for programmable model compression in Python. It comes with a set of built-in compression operators which may be used to compose complex compression schemes targeting specific combinations of DNN architecture, hardware platform, and optimization objective. To recover any accuracy lost during compression, Condensa uses a constrained optimization formulation of model compression and employs an Augmented Lagrangian-based algorithm as the optimizer. Status: Condensa is under active development, and bug reports, pull requests, and other feedback are all highly appreciated. See the contributions section below for more details on how to contribute.

deep-neural-networks model-pruning model-compression