经过测试的模型 - Amazon SageMaker
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经过测试的模型

以下可折叠部分提供有关经过 Amazon SageMaker Neo 团队测试的机器学习模型的信息。展开可折叠部分,根据您的框架去查看模型是否经过测试。

注意

这不是可以使用 Neo 编译的模型的完整列表。

参见支持的框架SageMaker Neo 支持的运算符,了解是否可以使用 SageMaker Neo 编译模型。

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

X86_Linux

X86_Windows

Alexnet

Resnet50

X

X

X

X

X

X

X

YOLOv2

X

X

X

X

X

YOLOv2_tiny

X

X

X

X

X

X

X

YOLOv3_416

X

X

X

X

X

YOLOv3_tiny

X

X

X

X

X

X

X

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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_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

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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

模型

ARMV8

ARM马里

Ambarella CV22

Ambarella CV25

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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

模型

ARMV8

ARM马里

Ambarella CV22

Ambarella CV25

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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)

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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)

模型

ARMV8

ARM马里

Ambarella CV22

Nvidia

Panorama

TI TDA4VM

高通 QCS6 03

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