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666DZY666 / micronet

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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、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

pytorch pruning convolutional-networks quantization xnor-net tensorrt model-compression bnn neuromorphic-computing group-convolution onnx network-in-network tensorrt-int8-python dorefa twn network-slimming integer-arithmetic-only quantization-aware-training post-training-quantization batch-normalization-fuse
  • Updated Jun 16, 2021
  • Python
sparseml

neuralmagic / sparseml

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Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

nlp sparsity tensorflow keras pytorch deep-learning-algorithms image-classification deep-learning-library pruning object-detection automl computer-vision-algorithms onnx deep-learning-models sparsification pruning-algorithms smaller-models model-sparsification sparsification-recipes recipe-driven-approaches
  • Updated Jul 9, 2021
  • Python

quic / aimet

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Add working example for compressing a Keras resnet18 model using Channel Pruning

quic-akhobare
quic-akhobare commented Dec 5, 2020
  • Create a new examples directory at the top-level
  • Use the API doc examples to apply Channel Pruning model compression to a Keras resnet18 model

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openvinotoolkit / nncf

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mehtadushy / SelecSLS-Pytorch

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Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

sparsity deep-learning efficient cnn pytorch imagenet pruning siggraph pytorch-implementation cvpr2019 efficient-architectures
  • Updated Jul 23, 2020
  • Python

SforAiDl / KD_Lib

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