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.

DarkNet

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

MXNet

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

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	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

ONNX

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

PyTorch (FP32)

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

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

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