《李宏毅深度学习教程》,PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
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Jan 28, 2023 - Jupyter Notebook
《李宏毅深度学习教程》,PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、reg…
A curated list of neural network pruning resources.
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
PaddleSlim is an open-source library for deep model compression and architecture search.
Inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
OpenMMLab Model Compression Toolbox and Benchmark.
Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference
[Preprint] Towards Any Structural Pruning
Config driven, easy backup cli for restic.
mobilev2-yolov5s剪枝、蒸馏,支持ncnn,tensorRT部署。ultra-light but better performence!
Embedded and mobile deep learning research resources
Neural Network Compression Framework for enhanced OpenVINO™ inference
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)
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