Modelos probados - Amazon SageMaker

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Modelos probados

Las siguientes secciones plegables proporcionan información sobre los modelos de aprendizaje automático que probó el equipo de Amazon SageMaker Neo. Amplíe la sección plegable en función de su estructura para comprobar si se ha probado un modelo.

nota

Esta no es una lista exhaustiva de los modelos que se pueden compilar con Neo.

Consulte Marcos admitidos los operadores compatibles con SageMaker Neo para averiguar si puede compilar su modelo con SageMaker Neo.

Modelos

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

AlexNet

Resnet 50

X

X

X

X

X

X

X

YOLOv2

X

X

X

X

X

YOLOv2_diminuto

X

X

X

X

X

X

X

YOLOv3_416

X

X

X

X

X

YOLOv3_tiny

X

X

X

X

X

X

X

Modelos

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

AlexNet

X

Densenet 121

X

DenseNet201

X

X

X

X

X

X

X

X

GoogLeNet

X

X

X

X

X

X

X

Inception v3

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_Small

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

SE_ 50_32x4d ResNext

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

Modelos

ARMV8

ARMMali

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

densenet 121

X

X

X

X

X

X

X

X

densenet 201

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

Modelos

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

resnet 18 contra 1

X

X

X

X

resnet18 v2

X

X

X

X

resnet50 v1

X

X

X

X

X

X

resnet50 v2

X

X

X

X

X

X

resnet 152 v1

X

X

X

X

resnet 152 v2

X

X

X

X

squeezenet 1.1

X

X

X

X

X

X

X

vgg19

X

X

Modelos

ARMV8

ARMMali

Ambarella CV22

Ambarella CV25

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

densenet 121

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

squeezenet 1.0

X

X

X

X

X

X

squeezenet 1.1

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

Modelos

ARMV8

ARMMali

Ambarella CV22

Ambarella CV25

Nvidia

Panorama

TI TDA4VM

Qualcomm 03 QCS6

X86_Linux

X86_Windows

densenet 201

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

mobilenet 130_v2

X

X

X

X

X

X

mobilenet 140_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

Modelos

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

Inception v3

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 V2

X

X

X

X

ResNet152

X

X

X

ResNet152 contra 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)

Modelos

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)

Modelos

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