wide-residual-networks 开源项目推荐（25 个）

bearpaw /pytorch-classification

Classification with PyTorch.

ansleliu /LightNet

LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

ajbrock /FreezeOut

通过逐步冻结层，加速神经网络训练

tugstugi /pytorch-speech-commands

Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge

mitmul /chainer-cifar10

Various CNN models for CIFAR10 with Chainer

szagoruyko /binary-wide-resnet

PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)

Curt-Park /handwritten_digit_recognition

Handwritten digit recognition with MNIST & Keras

yashkant /padam-tensorflow

Reproducing the paper "PADAM: Closing The Generalization Gap of Adaptive Gradient Methods In Training Deep Neural Networks" for the ICLR 2019 Reproducibility Challenge

ucla-vision /parle

该项目在 GitHub 上没有填写描述

nmamie /DNN-Watermarking

This repository was created as an implementation approach for a project on "Watermarking Deep Neural Networks".

sgrvinod /Wide-Residual-Nets-for-SETI

Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence

razorx89 /caffe-wrn-generator

Caffe Wide-Residual-Network (WRN) Generator

Gurupradeep /CIFAR-10-Object-Recognition-in-Images

Implementing and comparing performance of various deep learning models on CIFAR-10 dataset

JRC1995 /Wide-Residual-Network

Implementation of a Wide Residual Network on Tensorflow for Image Classification. Trained and tested on Cifar10 dataset.

astha-chem /Decathlon-Residual-Adapters-Keras

Decathlon Multiple Visual Domains with Residual Adapters in Keras

paradoxysm /wideresnet

Wide Residual Networks in Keras and PyTorch

ba-san /Crowd-Analysis-with-CNN

Estimate number and location of people in crowd image

alvesmarcos /DL-Character-Description

A model to classify gender, age and ethnicity of a person

sverneka /wide-resnet-cifar100-kaggle

Wide-Resnet-Ensemble implementation for CIFAR100 kaggle submission: >83.97% on kaggle private leaderboard

YASH-13-lab /segmentation-cityscape

🚦 Train and evaluate semantic segmentation models on the Cityscapes dataset using PyTorch with DeepLabV3/DeepLabV3+ for precise pixel-wise predictions.