Tested Models

The following collapsible sections provide information about machine learning models that were tested by the Amazon SageMaker Neo team. Expand the collapsible section based on your framework to check if a model was tested.

Note

This is not a comprehensive list of models that can be compiled with Neo.

See Supported Frameworks and SageMaker Neo Supported Operators to find out if you can compile your model with SageMaker Neo.

DarkNet

Models	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

Models	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

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

Models	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)

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

Models	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

Models	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)

Models	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)

Models	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