Image recognition

Image generation

Image segmentation

tutorial.anomaly_detection.sin_wave_anomaly_detection

Anormary detection from sin wave using auto encoder

tutorial.object_detection.synthetic_image_object_detection

Object detection using synthetic image data including ellipse, triangle, rectangle and pentagon

classification.iris.iris

Vector classification on iris flower dataset

tutorial.basics.01_logistic_regression

Image binary classification using the simplest one-layer neural networks

tutorial.basics.02_binary_cnn

Image binary classification using 4-layer convolutional neural networks

tutorial.basics.10_deep_mlp

Deep neural networks

tutorial.basics.12_residual_learning

Image classification using residual networks on MNIST dataset

tutorial.binary_networks.binary_connect_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_connect_mnist_LeNet

CNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_net_mnist_MLP

DNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_net_mnist_LeNet

CNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_weight_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.recurrent_neural_networks.elman_net

The simplest recurrent neural networks

tutorial.recurrent_neural_networks.bidirectional_elman_net

Bi-directional recurrent neural networks

tutorial.recurrent_neural_networks.elman_net_with_attention

Recurrent neural networks with attention

tutorial.recurrent_neural_networks.gated_recurrent_unit(GRU)

Gated recurrent unit (GRU)

tutorial.recurrent_neural_networks.long_short_term_memory(LSTM)

Long short term memory (LSTM)

tutorial.recurrent_neural_networks.stacked_GRU

Recurrent neural networks with 2 GRUs

tutorial.image_classification.hand-sign

Example of 4-class hand-sign image classification for SPRESENSE

tutorial.explainable_dl.01_visualize_weight_of_feature

Visualization method of importance of input data by learning weight of input data

tutorial.explainable_dl.02_l1_regularization

Visualization method of importance of input data using L1 regularization

tutorial.explainable_dl.03_attention

Visualization method of attention area

tutorial.explainable_dl.04_inference_result_at_each_layer

Visualization method to check output of each data path

image_recognition.MNIST.LeNet

Image classification using 4-layer convolutional neural networks on MNIST dataset

image_recognition.MNIST.semi_supervised_learning_VAT

Semi-supervised learning using variational auto encoder

image_recognition.FashionMNIST.LeNet

Image classification using 4-layer convolutional neural networks on Fashion-MNIST dataset

image_recognition.CIFAR10.resnet.resnet-110-deepmil

Weakly supervised image localizaion

image_recognition.CIFAR10.resnet.resnet-110-mixup

Image augmentation by blending 2 images

image_recognition.CIFAR10.resnet.resnet-110

Image classification using residual networks on CIFAR-10 dataset

image_recognition.CIFAR100.resnet.resnet-110

Image classification using residual networks on CIFAR-100 dataset

image_recognition.ILSVRC2012.densenet.densenet-161

Light weight neural networks that express shallow and deep networks simultaneously

image_recognition.ILSVRC2012.residual networks.resnet-101

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-152

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-18

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-34

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-50

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.resnext.resnext-101

Residual networks with grouped convolution

image_recognition.ILSVRC2012.shufflenet.shufflenet-0.5x

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.shufflenet.shufflenet-2.0x

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.shufflenet.shufflenet

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.squeezenet.squeezenet11

Light weight convolutional neural networks with bottle-neck structure

image_recognition.ILSVRC2012.vgg.vgg-11

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.vgg.vgg-13

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.vgg.vgg-16

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.alexnet

Neural networks for image classification which is the winner of the ImageNet challenge 2012

image_recognition.ILSVRC2012.GoogLeNet

Neural networks for image classification which is the winner of the ImageNet challenge 2014

image_recognition.ILSVRC2012.nin

Network in networks

tutorial.basics.06_auto_encoder

Auto encoder

tutorial.basics.11_deconvolution

Convolutional auto encoder

tutorial.recurrent_neural_networks.LSTM_auto_encoder

Auto encoder using LSTM

image_generation.mnist_dcgan

Deep Convolutional Generative Adversarial Networks(DCGAN)

image_generation.mnist_dcgan_with_label

Deep Convolutional Generative Adversarial Networks(DCGAN)

image_generation.mnist_vae

Variational auto encoder (VAE)

tutorial.semantic_segmentation.binary_semantic_segmentation

Binary segmentation to separate letters and background

tutorial.semantic_segmentation.unetlike_125px

20 class semantic segmentation using U-Net

tutorial.semantic_segmentation.unetlike_125px_person

2 class semantic segmentation using U-Net

CNN

ResNet

DNN

BNN

RNN

tutorial.basics.01_logistic_regression

Image binary classification using the simplest one-layer neural networks

tutorial.basics.06_auto_encoder

Auto encoder

tutorial.basics.11_deconvolution

Convolutional auto encoder

tutorial.anomaly_detection.sin_wave_anomaly_detection

Anormary detection from sin wave using auto encoder

tutorial.basics.02_binary_cnn

Image binary classification using 4-layer convolutional neural networks

tutorial.image_classification.hand-sign

Example of 4-class hand-sign image classification for SPRESENSE

tutorial.explainable_dl.04_inference_result_at_each_layer

Visualization method to check output of each data path

tutorial.semantic_segmentation.binary_semantic_segmentation

Binary segmentation to separate letters and background

image_recognition.FashionMNIST.LeNet

Image classification using 4-layer convolutional neural networks on Fashion-MNIST dataset

image_recognition.MNIST.LeNet

Image classification using 4-layer convolutional neural networks on MNIST dataset

image_recognition.CIFAR10.resnet.resnet-110

Image classification using residual networks on CIFAR-10 dataset

image_recognition.CIFAR100.resnet.resnet-110

Image classification using residual networks on CIFAR-100 dataset

image_recognition.ILSVRC2012.densenet.densenet-161

Light weight neural networks that express shallow and deep networks simultaneously

image_recognition.ILSVRC2012.shufflenet.shufflenet-0.5x

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.shufflenet.shufflenet-2.0x

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.shufflenet.shufflenet

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.squeezenet.squeezenet11

Light weight convolutional neural networks with bottle-neck structure

image_recognition.ILSVRC2012.vgg.vgg-11

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.vgg.vgg-13

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.vgg.vgg-16

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.alexnet

Neural networks for image classification which is the winner of the ImageNet challenge 2012

image_recognition.ILSVRC2012.GoogLeNet

Neural networks for image classification which is the winner of the ImageNet challenge 2014

image_recognition.ILSVRC2012.nin

Network in networks

tutorial.basics.12_residual_learning

Image classification using residual networks on MNIST dataset

image_recognition.ILSVRC2012.residual networks.resnet-18

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-34

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-50

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-101

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-152

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.resnext.resnext-101

Residual networks with grouped convolution

tutorial.binary_networks.binary_connect_mnist_LeNet

CNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_net_mnist_LeNet

CNN that consumes much less memory and computation by weight and data path binalization

tutorial.semantic_segmentation.unetlike_125px

20 class semantic segmentation using U-Net

tutorial.semantic_segmentation.unetlike_125px_person

2 class semantic segmentation using U-Net

image_generation.mnist_dcgan

Deep Convolutional Generative Adversarial Networks(DCGAN)

image_generation.mnist_dcgan_with_label

Deep Convolutional Generative Adversarial Networks(DCGAN)

image_recognition.CIFAR10.resnet.resnet-110-deepmil

Weakly supervised image localizaion

image_recognition.CIFAR10.resnet.resnet-110-mixup

Image augmentation by blending 2 images

tutorial.object_detection.synthetic_image_object_detection

Object detection using synthetic image data including ellipse, triangle, rectangle and pentagon

tutorial.basics.12_residual_learning

Image classification using residual networks on MNIST dataset

image_recognition.ILSVRC2012.residual networks.resnet-18

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-34

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-50

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-101

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-152

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.resnext.resnext-101

Residual networks with grouped convolution

tutorial.basics.10_deep_mlp

Deep neural networks

tutorial.binary_networks.binary_connect_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_net_mnist_MLP

DNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_weight_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.explainable_dl.01_visualize_weight_of_feature

Visualization method of importance of input data by learning weight of input data

tutorial.explainable_dl.02_l1_regularization

Visualization method of importance of input data using L1 regularization

tutorial.explainable_dl.03_attention

Visualization method of attention area

classification.iris.iris

Vector classification on iris flower dataset

image_generation.mnist_vae

Variational auto encoder (VAE)

image_recognition.MNIST.semi_supervised_learning_VAT

Semi-supervised learning using variational auto encoder

tutorial.binary_networks.binary_connect_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_connect_mnist_LeNet

CNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_net_mnist_MLP

DNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_net_mnist_LeNet

CNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_weight_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.recurrent_neural_networks.elman_net

The simplest recurrent neural networks

tutorial.recurrent_neural_networks.bidirectional_elman_net

Bi-directional recurrent neural networks

tutorial.recurrent_neural_networks.elman_net_with_attention

Recurrent neural networks with attention

tutorial.recurrent_neural_networks.gated_recurrent_unit(GRU)

Gated recurrent unit (GRU)

tutorial.recurrent_neural_networks.long_short_term_memory(LSTM)

Long short term memory (LSTM)

tutorial.recurrent_neural_networks.LSTM_auto_encoder

Auto encoder using LSTM

tutorial.recurrent_neural_networks.stacked_GRU

Recurrent neural networks with 2 GRUs

small MNIST

MNIST

ImageNet

CIFAR-10/100

tutorial.image_classification.hand-sign

Example of 4-class hand-sign image classification for SPRESENSE

tutorial.semantic_segmentation.binary_semantic_segmentation

Binary segmentation to separate letters and background

tutorial.semantic_segmentation.unetlike_125px

20 class semantic segmentation using U-Net

tutorial.semantic_segmentation.unetlike_125px_person

2 class semantic segmentation using U-Net

tutorial.anomaly_detection.sin_wave_anomaly_detection

Anormary detection from sin wave using auto encoder

tutorial.object_detection.synthetic_image_object_detection

Object detection using synthetic image data including ellipse, triangle, rectangle and pentagon

classification.iris.iris

Vector classification on iris flower dataset

tutorial.basics.01_logistic_regression

Image binary classification using the simplest one-layer neural networks

tutorial.basics.02_binary_cnn

Image binary classification using 4-layer convolutional neural networks

tutorial.basics.06_auto_encoder

Auto encoder

tutorial.basics.10_deep_mlp

Deep neural networks

tutorial.basics.11_deconvolution

Convolutional auto encoder

tutorial.recurrent_neural_networks.elman_net

The simplest recurrent neural networks

tutorial.recurrent_neural_networks.bidirectional_elman_net

Bi-directional recurrent neural networks

tutorial.recurrent_neural_networks.elman_net_with_attention

Recurrent neural networks with attention

tutorial.recurrent_neural_networks.gated_recurrent_unit(GRU)

Gated recurrent unit (GRU)

tutorial.recurrent_neural_networks.long_short_term_memory(LSTM)

Long short term memory (LSTM)

tutorial.recurrent_neural_networks.LSTM_auto_encoder

Auto encoder using LSTM

tutorial.recurrent_neural_networks.stacked_GRU

Recurrent neural networks with 2 GRUs

tutorial.explainable_dl.01_visualize_weight_of_feature

Visualization method of importance of input data by learning weight of input data

tutorial.explainable_dl.02_l1_regularization

Visualization method of importance of input data using L1 regularization

tutorial.explainable_dl.03_attention

Visualization method of attention area

tutorial.explainable_dl.04_inference_result_at_each_layer

Visualization method to check output of each data path

tutorial.basics.12_residual_learning

Image classification using residual networks on MNIST dataset

tutorial.binary_networks.binary_connect_mnist_MLP

DNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_connect_mnist_LeNet

CNN that consumes much less memory by weight binalization

tutorial.binary_networks.binary_net_mnist_MLP

DNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_net_mnist_LeNet

CNN that consumes much less memory and computation by weight and data path binalization

tutorial.binary_networks.binary_weight_mnist_MLP

DNN that consumes much less memory by weight binalization

image_recognition.MNIST.LeNet

Image classification using 4-layer convolutional neural networks on MNIST dataset

image_recognition.MNIST.semi_supervised_learning_VAT

Semi-supervised learning using variational auto encoder

image_recognition.FashionMNIST.LeNet

Image classification using 4-layer convolutional neural networks on Fashion-MNIST dataset

image_generation.mnist_dcgan

Deep Convolutional Generative Adversarial Networks(DCGAN)

image_generation.mnist_dcgan_with_label

Deep Convolutional Generative Adversarial Networks(DCGAN)

image_generation.mnist_vae

Variational auto encoder (VAE)

image_recognition.ILSVRC2012.densenet.densenet-161

Light weight neural networks that express shallow and deep networks simultaneously

image_recognition.ILSVRC2012.residual networks.resnet-101

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-152

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-18

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-34

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.residual networks.resnet-50

Neural networks for image classification which is the winner of the ImageNet challenge 2015

image_recognition.ILSVRC2012.resnext.resnext-101

Residual networks with grouped convolution

image_recognition.ILSVRC2012.shufflenet.shufflenet-0.5x

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.shufflenet.shufflenet-2.0x

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.shufflenet.shufflenet

Light weight convolutional neural networks with shuffle structure

image_recognition.ILSVRC2012.squeezenet.squeezenet11

Light weight convolutional neural networks with bottle-neck structure

image_recognition.ILSVRC2012.vgg.vgg-11

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.vgg.vgg-13

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.vgg.vgg-16

Neural networks for image classification which is the winner of the ImageNet challenge 2013

image_recognition.ILSVRC2012.alexnet

Neural networks for image classification which is the winner of the ImageNet challenge 2012

image_recognition.ILSVRC2012.GoogLeNet

Neural networks for image classification which is the winner of the ImageNet challenge 2014

image_recognition.ILSVRC2012.nin

Network in networks

image_recognition.CIFAR10.resnet.resnet-110-deepmil

Weakly supervised image localizaion

image_recognition.CIFAR10.resnet.resnet-110-mixup

Image augmentation by blending 2 images

image_recognition.CIFAR10.resnet.resnet-110

Image classification using residual networks on CIFAR-10 dataset

image_recognition.CIFAR100.resnet.resnet-110

Image classification using residual networks on CIFAR-100 dataset

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