Modelli testati - Amazon SageMaker

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Modelli testati

Le seguenti sezioni pieghevoli forniscono informazioni sui modelli di machine learning testati dal team di Amazon SageMaker Neo. Espandi la sezione comprimibile in base al tuo framework per verificare se un modello è stato testato.

Nota

Questo non è un elenco completo di modelli che possono essere compilati con Neo.

Consulta SageMaker Neo Framework supportati Supported Operators per scoprire se puoi compilare il tuo modello con SageMaker Neo.

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

Alexnet

ResNet50

X

X

X

X

X

X

X

YOLOv2

X

X

X

X

X

YOLOv2_minuscolo

X

X

X

X

X

X

X

YOLOv3_416

X

X

X

X

X

YOLOv3_minuscolo

X

X

X

X

X

X

X

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

Alexnet

X

Densenet121

X

DenseNet201

X

X

X

X

X

X

X

X

GoogLeNet

X

X

X

X

X

X

X

InceptionV3

X

X

X

X

X

MobileNet0,75

X

X

X

X

X

X

MobileNet1,0

X

X

X

X

X

X

X

MobileNetV2_0,5

X

X

X

X

X

X

MobileNetV2_1.0

X

X

X

X

X

X

X

X

X

MobileNetV3_Large

X

 X

X

X

X

X

X

X

X

MobileNetV3_Piccolo

X

X

X

X

X

X

X

X

X

ResNeSt50

X

X

X

X

ResNet18 v1

X

X

X

X

X

X

X

ResNet18_v2

X

X

X

X

X

X

ResNet50_v1

X

X

X

X

X

X

X

X

ResNet50_v2

 X

X

X

X

X

X

X

X

ResNext101_32x4d

ResNext50_32x4d

X

X

X

X

X

X

SENet_154

X

X

X

X

X

ResNextSE_ 50_32x4d

X

X

X

X

 X

X

X

SqueezeNet1,0

X

X

X

X

X

X

X

SqueezeNet1.1

X

X

X

X

X

X

X

X

VGG11

X

X

X

X

X

X

X

Xception

X

X

X

X

X

X

X

X

darknet53

X

X

X

X

X

X

X

resnet18_v1b_0.89

X

X

X

X

X

X

resnet50_v1d_0.11

X

X

X

X

X

X

resnet50_v1d_0.86

X

X

X

X

X

X

X

X

ssd_512_mobilenet1.0_coco

X

X

X

X

X

X

X

ssd_512_mobilenet1.0_voc

X

X

X

X

X

X

X

ssd_resnet50_v1

X

X

X

X

X

X

yolo3_darknet53_coco

X

X

X

X

X

yolo3_mobilenet1.0_coco

X

X

X

X

X

X

X

deeplab_resnet50

X

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

densenet121

X

X

X

X

X

X

X

X

densenet201

X

X

X

X

X

X

X

inception_v3

X

X

X

X

X

X

X

mobilenet_v1

X

X

X

X

X

X

X

X

mobilenet_v2

X

X

X

X

X

X

X

X

resnet152_v1

X

X

X

resnet152_v2

X

X

X

resnet50_v1

X

X

X

X

X

X

X

resnet50_v2

X

X

X

X

X

X

X

X

vgg16

X

X

X

X

X

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

alexnet

X

mobilenetv2-1.0

X

X

X

X

X

X

X

X

resnet18v1

X

X

X

X

resnet18v2

X

X

X

X

resnet50v1

X

X

X

X

X

X

resnet50v2

X

X

X

X

X

X

resnet152v1

X

X

X

X

resnet152v2

X

X

X

X

squeezenet1.1

X

X

X

X

X

X

X

vgg19

X

X

Modelli

ARMV8

ARMMali

Ambarella CV22

Ambarella CV25

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

densenet121

X

X

X

X

X

X

X

X

X

inception_v3

X

X

X

X

X

X

resnet152

X

X

X

X

resnet18

X

X

X

X

X

X

resnet50

X

X

X

X

X

X

X

X

squeezenet1.0

X

X

X

X

X

X

squeezenet1.1

X

X

X

X

X

X

X

X

X

yolov4

X

X

yolov5

X

X

X

fasterrcnn_resnet50_fpn

X

X

maskrcnn_resnet50_fpn

X

X

TensorFlow

Modelli

ARMV8

ARMMali

Ambarella CV22

Ambarella CV25

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

densenet201

X

X

X

X

X

X

X

X

X

inception_v3

X

X

X

X

X

X

X

X

mobilenet100_v1

X

X

X

X

X

X

X

mobilenet100_v2.0

X

X

X

X

X

X

X

X

mobilenet130_v2

X

X

X

X

X

X

mobilenet140_v2

X

X

X

X

X

X

X

X

resnet50_v1.5

X

X

X

X

X

X

X

resnet50_v2

X

X

X

X

X

X

X

X

X

squeezenet

X

X

X

X

X

X

X

X

X

mask_rcnn_inception_resnet_v2

X

ssd_mobilenet_v2

X

X

faster_rcnn_resnet50_lowproposals

X

rfcn_resnet101

X

TensorFlow.Keras

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

DenseNet121

X

X

X

X

X

X

X

DenseNet201

X

X

X

X

X

X

InceptionV3

X

X

X

X

X

X

X

MobileNet

X

X

X

X

X

X

X

MobileNetv2

X

X

X

X

X

X

X

NASNetLarge

X

X

X

X

NASNetMobile

X

X

X

X

X

X

X

ResNet101

X

X

X

X

ResNet101 V 2

X

X

X

X

ResNet152

X

X

X

ResNet152 contro 2

X

X

X

ResNet50

X

X

X

X

X

X

ResNet50 V2

X

X

X

X

X

X

X

VGG16

X

X

X

X

Xception

X

X

X

X

X

X

X
TensorFlow-Lite (FP32)

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

i.MX 8M Plus

densenet_2018_04_27

X

X

X

X

X

inception_resnet_v2_2018_04_27

X

X

 X

X

inception_v3_2018_04_27

X

X

X

X

X

inception_v4_2018_04_27

X

X

X

X

X

mnasnet_0.5_224_09_07_2018

X

X

X

X

X

mnasnet_1.0_224_09_07_2018

X

X

X

X

X

mnasnet_1.3_224_09_07_2018

X

X

X

X

X

mobilenet_v1_0.25_128

X

X

X

X

X

X

mobilenet_v1_0.25_224

X

X

X

X

X

X

mobilenet_v1_0.5_128

X

X

X

X

X

X

mobilenet_v1_0.5_224

X

X

X

X

X

X

mobilenet_v1_0.75_128

X

X

X

X

X

X

mobilenet_v1_0.75_224

X

X

X

X

X

X

mobilenet_v1_1.0_128

X

X

X

X

X

X

mobilenet_v1_1.0_192

X

X

X

X

X

X

mobilenet_v2_1.0_224

X

X

X

X

X

X

resnet_v2_101

X

X

X

X

squeezenet_2018_04_27

X

X

X

X

X

TensorFlow-Lite (INT8)

Modelli

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

i.MX 8M Plus

inception_v1

X

X

inception_v2

X

X

inception_v3

X

X

X

X

X

inception_v4_299

X

X

X

X

X

mobilenet_v1_0.25_128

X

X

X

X

mobilenet_v1_0.25_224

X

X

X

X

mobilenet_v1_0.5_128

X

X

X

X

mobilenet_v1_0.5_224

X

X

X

X

mobilenet_v1_0.75_128

X

X

X

X

mobilenet_v1_0.75_224

X

X

X

X

X

mobilenet_v1_1.0_128

X

X

X

X

mobilenet_v1_1.0_224

X

X

X

X

X

mobilenet_v2_1.0_224

X

X

X

X

X

deeplab-v3_513

X