PyTorch Implementation of Mobilenet Variants including support for residual connections, group convolutions and squeeze-excite blocks
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Updated
Jul 4, 2018 - Python
PyTorch Implementation of Mobilenet Variants including support for residual connections, group convolutions and squeeze-excite blocks
Code repository for paper "Efficient Structured Pruning and Architecture Searching for Group Convolution" https://arxiv.org/abs/1811.09341
Tensorflow 2 / tf.keras port of keras-gcnn.
Tensorflow 2 port of Group Equivariant Convolutional Neural Networks
Implementation of "Fully Learnable Group Convolution for Acceleration of Deep Neural Networks", CVPR'19
Code repository for the paper "Attentive Group Equivariant Convolutional Neural Networks" published at ICML 2020. https://arxiv.org/abs/2002.03830
PyTorch implementation of Dynamic Grouping Convolution and Groupable ConvNet with pre-trained G-ResNeXt models
Code repository of the paper "Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series, TMLR" https://arxiv.org/abs/2006.05259
ShuffleNet Implementation in TensorFlow
E(2)-Equivariant CNNs Library for Pytorch
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…
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