Oleg Sémery's starred repositories
pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
awesome-tensorflow
TensorFlow - A curated list of dedicated resources http://tensorflow.org
Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
the-incredible-pytorch
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
deep-learning-model-convertor
The convertor/conversion of deep learning models for different deep learning frameworks/softwares.
proxylessnas
[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Efficient-Segmentation-Networks
Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)
pytorch2keras
PyTorch to Keras model convertor
Awesome-MXNet
A curated list of MXNet examples, tutorials and blogs.
CondenseNet
CondenseNet: Light weighted CNN for mobile devices
functional-zoo
PyTorch and Tensorflow functional model definitions
awesome-pytorch
Awesome PyTorch
PyramidNet-PyTorch
A PyTorch implementation for PyramidNets (Deep Pyramidal Residual Networks, https://arxiv.org/abs/1610.02915)
MSDNet-GCN
ICLR 2018 reproducibility challenge - Multi-Scale Dense Convolutional Networks for Efficient Prediction
gluon2pytorch
Gluon to PyTorch deep neural network model converter
AirNet-PyTorch
Implementation of the paper ''Attention Inspiring Receptive-fields Network''
awesome-deeplearning
Because Deep Learning is awesome.
FD-MobileNet
This repo contains code for *FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy*.