Model yang Diuji - Amazon SageMaker

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Model yang Diuji

Bagian yang dapat dilipat berikut memberikan informasi tentang model pembelajaran mesin yang diuji oleh tim Amazon SageMaker Neo. Perluas bagian yang dapat dilipat berdasarkan kerangka kerja Anda untuk memeriksa apakah model telah diuji.

catatan

Ini bukan daftar lengkap model yang dapat dikompilasi dengan Neo.

Lihat Kerangka Kerja yang Didukung dan SageMaker Neo Operator yang Didukung untuk mengetahui apakah Anda dapat mengkompilasi model Anda dengan SageMaker Neo.

Model

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_kecil

X

X

X

X

X

X

X

YOLOv3_416

X

X

X

X

X

YOLOv3_kecil

X

X

X

X

X

X

X

Model

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_Besar

X

 X

X

X

X

X

X

X

X

MobileNetV3_Kecil

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

Model

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

Model

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

Model

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

Model

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

meremas

X

X

X

X

X

X

X

X

X

topeng_rcnn_inception_resnet_v2

X

ssd_mobilenet_v2

X

X

lebih cepat_rcnn_resnet50_lowproposal

X

rfcn_resnet101

X

TensorFlow.Keras

Model

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

ResNet101V2

X

X

X

X

ResNet152

X

X

X

ResNet152v2

X

X

X

ResNet50

X

X

X

X

X

X

ResNet50V2

X

X

X

X

X

X

X

VGG16

X

X

X

X

Xception

X

X

X

X

X

X

X
TensorFlow-Lite (FP32)

Model

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)

Model

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