Modèles testés

Les sections réductibles suivantes fournissent des informations sur les modèles de machine learning qui ont été testés par l'équipe Amazon SageMaker Neo. Développez la section réductible en fonction de votre cadre pour vérifier si un modèle a été testé.

Note

Ceci n'est pas une liste complète de modèles qui peuvent être compilés avec Neo.

Veillez consulter Cadres pris en charge et SageMaker Neo Supported Operators (Opérateurs pris en charge par SageMaker Neo) pour savoir si vous pouvez compiler votre modèle avec SageMaker Neo.

DarkNet

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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

MXNet

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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_ResNext50_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

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	X86_Linux	X86_Windows
denseet121	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

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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)

Modèles	ARM V8	ARM Mali	Ambarella CV22	Ambarella CV25	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	X86_Linux	X86_Windows
denseet121	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

Modèles	ARM V8	ARM Mali	Ambarella CV22	Ambarella CV25	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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

TensorFlow-Lite (FP32)

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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)

Modèles	ARM V8	ARM Mali	Ambarella CV22	Nvidia	Panorama	TI TDA4VM	Qualcomm QCS603	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