Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

Stars: ✭ 2,423 (+1792.97%)

Mutual labels: resnet, densenet, resnext

Tianchi Medical Lungtumordetect

天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

Stars: ✭ 314 (+145.31%)

Mutual labels: classification, resnet, densenet

Chainer Cifar10

Various CNN models for CIFAR10 with Chainer

Stars: ✭ 134 (+4.69%)

Mutual labels: resnet, densenet, cifar10

Segmentation models

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Stars: ✭ 3,575 (+2692.97%)

Mutual labels: resnet, densenet, resnext

Caffe Model

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

Stars: ✭ 1,258 (+882.81%)

Mutual labels: classification, resnet, resnext

Keras Idiomatic Programmer

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Stars: ✭ 720 (+462.5%)

Mutual labels: resnet, densenet, resnext

Cifar Zoo

PyTorch implementation of CNNs for CIFAR benchmark

Stars: ✭ 584 (+356.25%)

Mutual labels: resnet, densenet, resnext

Pytorch classification

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

Stars: ✭ 395 (+208.59%)

Mutual labels: resnet, densenet, resnext

Basic cnns tensorflow2

A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).

Stars: ✭ 374 (+192.19%)

Mutual labels: resnet, densenet, resnext

Classification models

Classification models trained on ImageNet. Keras.

Stars: ✭ 938 (+632.81%)

Mutual labels: resnet, densenet, resnext

Pytorch Asr

ASR with PyTorch

Stars: ✭ 124 (-3.12%)

Mutual labels: speech-recognition, resnet, densenet

Segmentationcpp

A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.

Stars: ✭ 49 (-61.72%)

Mutual labels: resnet, resnext

Tensornets

High level network definitions with pre-trained weights in TensorFlow

Stars: ✭ 982 (+667.19%)

Mutual labels: resnet, densenet

Pretrained Models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

Stars: ✭ 8,318 (+6398.44%)

Mutual labels: resnet, resnext

Resnet On Cifar10

Reimplementation ResNet on cifar10 with caffe

Stars: ✭ 123 (-3.91%)

Mutual labels: resnet, cifar10

Randwire tensorflow

tensorflow implementation of Exploring Randomly Wired Neural Networks for Image Recognition

Stars: ✭ 29 (-77.34%)

Mutual labels: classification, cifar10

Gluon2pytorch

Gluon to PyTorch deep neural network model converter

Stars: ✭ 70 (-45.31%)

Mutual labels: resnet, densenet

Mlbox

MLBox is a powerful Automated Machine Learning python library.

Stars: ✭ 1,199 (+836.72%)

Mutual labels: kaggle, classification

View All Similar Projects ➔

Convolutional neural networks for Google speech commands data set with PyTorch.

General

We, xuyuan and tugstugi, have participated in the Kaggle competition TensorFlow Speech Recognition Challenge and reached the 10-th place. This repository contains a simplified and cleaned up version of our team's code.

Features

1x32x32 mel-spectrogram as network input
single network implementation both for CIFAR10 and Google speech commands data sets
faster audio data augmentation on STFT
Kaggle private LB scores evaluated on 150.000+ audio files

Results

Due to time limit of the competition, we have trained most of the nets with sgd using ReduceLROnPlateau for 70 epochs. For the training parameters and dependencies, see TRAINING.md. Earlier stopping the train process will sometimes produce a better score in Kaggle.

^_Model	^{_{CIFAR10 test set accuracy}}	^{_{Speech Commands test set accuracy}}	^{_{Speech Commands test set accuracy with crop}}	^{_{Speech Commands Kaggle private LB score}}	^{_{Speech Commands Kaggle private LB score with crop}}	^_Remarks
^{_{VGG19 BN}}	^_93.56%	^_97.337235%	^_97.527432%	^_0.87454	^_0.88030
^_ResNet32	^_-	^_96.181419%	^_96.196050%	^_0.87078	^_0.87419
^_WRN-28-10	^_-	^_97.937089%	^_97.922458%	^_0.88546	^_0.88699
^{_{WRN-28-10-dropout}}	^_96.22%	^_97.702999%	^_97.717630%	^_0.89580	^_0.89568
^_WRN-52-10	^_-	^_98.039503%	^_97.980980%	^_0.88159	^_0.88323	^{_{another trained model has 97.52%/0.89322}}
^{_{ResNext29 8x64}}	^_-	^_97.190929%	^_97.161668%	^_0.89533	^_0.89733	^{_{our best model during competition}}
^_DPN92	^_-	^_97.190929%	^_97.249451%	^_0.89075	^_0.89286
^{_{DenseNet-BC (L=100, k=12)}}	^_95.52%	^_97.161668%	^_97.147037%	^_0.88946	^_0.89134
^{_{DenseNet-BC (L=190, k=40)}}	^_-	^_97.117776%	^_97.147037%	^_0.89369	^_0.89521

Results with Mixup

After the competition, some of the networks were retrained using mixup: Beyond Empirical Risk Minimization by Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin and David Lopez-Paz.

^_Model	^{_{CIFAR10 test set accuracy}}	^{_{Speech Commands test set accuracy}}	^{_{Speech Commands test set accuracy with crop}}	^{_{Speech Commands Kaggle private LB score}}	^{_{Speech Commands Kaggle private LB score with crop}}	^_Remarks
^{_{VGG19 BN}}	^_-	^_97.483541%	^_97.542063%	^_0.89521	^_0.89839
^_WRN-52-10	^_-	^_97.454279%	^_97.498171%	^_0.90273	^_0.90355	^{_{same score as the 16-th place in Kaggle}}

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].

Stars: ✭ 128

Visit Git Page 🔗Visit User Page 🔗Visit Issues Page (0) 🔗