Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

# Getestete Modelle
<a name="neo-supported-edge-tested-models"></a>

Die folgenden zusammenklappbaren Abschnitte enthalten Informationen zu Modellen für maschinelles Lernen, die vom Amazon SageMaker Neo-Team getestet wurden. Erweitern Sie den zusammenklappbaren Abschnitt auf der Grundlage Ihres Frameworks, um zu überprüfen, ob ein Modell getestet wurde.

**Anmerkung**  
Dies ist keine umfassende Liste von Modellen, die mit Neo kompiliert werden können.

Unter [Unterstützte Frameworks](neo-supported-devices-edge-frameworks.md) und von [SageMaker AI Neo unterstützte Operatoren](https://aws.amazon.com/releasenotes/sagemaker-neo-supported-frameworks-and-operators/) erfahren Sie, ob Sie Ihr Modell mit SageMaker Neo kompilieren können.

## DarkNet
<a name="collapsible-section-01"></a>


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| Alexnet |  |  |  |  |  |  |  |  |  | 
| Resnet 50 | X | X |  | X | X | X |  | X | X | 
| Yolo V 2 |  |  |  | X | X | X |  | X | X | 
| YoloV2\_Tiny | X | X |  | X | X | X |  | X | X | 
| Yolov 3\_416 |  |  |  | X | X | X |  | X | X | 
| Yolov 3\_Tiny | X | X |  | X | X | X |  | X | X | 

## MXNet
<a name="collapsible-section-02"></a>


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| Alexnet |  |  | X |  |  |  |  |  |  | 
| Dichtes Netz 121 |  |  | X |  |  |  |  |  |  | 
| DenseNet201 | X | X | X | X | X | X |  | X | X | 
| GoogLeNet | X | X |  | X | X | X |  | X | X | 
| Inception V3 |  |  |  | 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\_Groß | X | X | X | X | X | X | X | X | X | 
| MobileNetV3\_Klein | 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 | 
| VGG 11 | X | X | X | X | X |  |  | X | X | 
| Ausnahme | X | X | X | X | X | X |  | X | X | 
| Darknet 53 | 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
<a name="collapsible-section-03"></a>


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| dichtes Netz 121 | X | X | X | X | X | X |  | X | X | 
| densenet201 | X | X | X | X | X | X |  |  | X | 
| Anfang\_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 | 
| vgg 16 |  |  | X | X | X |  |  | X | X | 

## ONNX
<a name="collapsible-section-04"></a>


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| alexNet |  |  | X |  |  |  |  |  |  | 
| mobilenet Version 2-1.0 | X | X | X | X | X | X |  | X | X | 
| resnet 18 v1 | X |  |  | X | X |  |  |  | X | 
| resnet 18 v2 | X |  |  | X | X |  |  |  | X | 
| resnet 50 v1 | X |  | X | X | X |  |  | X | X | 
| resnet 50 v2 | X |  | X | X | X |  |  | X | X | 
| resnet 152 v1 |  |  |  | X | X | X |  |  | X | 
| resnet 152 v2 |  |  |  | X | X | X |  |  | X | 
| squeezenet1.1 | X |  | X | X | X | X |  | X | X | 
| vgg 19 |  |  | X |  |  |  |  |  | X | 

## PyTorch (FP32)
<a name="collapsible-section-05"></a>


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Ambarella CV-25 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| dichtes Netz 121 | X | X | X | X | X | X | X |  | X | X | 
| Anfang\_v3 |  | X |  |  | X | X | X |  | X | X | 
| resnet152 |  |  |  |  | X | X | X |  |  | X | 
| resnet18 | X | X |  |  | X | X | X |  |  | X | 
| resnet 50 | X | X | X | X | X | X |  |  | X | X | 
| Squeezenet 1.0 | X | X |  |  | X | X | X |  |  | X | 
| squeezenet1.1 | X | X | X | X | X | X | X |  | X | X | 
| Yolov 4 |  |  |  |  | X | X |  |  |  |  | 
| Yolov 5 |  |  |  | X | X | X |  |  |  |  | 
| schnelleres rcnn\_resnet50\_fpn |  |  |  |  | X | X |  |  |  |  | 
| maskieren Sie rcnn\_resnet50\_fpn |  |  |  |  | X | X |  |  |  |  | 

## TensorFlow
<a name="collapsible-section-06"></a>

------
#### [ TensorFlow ]


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Ambarella CV-25 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| dichtes Netz 201 | X | X | X | X | X | X | X |  | X | X | 
| Anfang\_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 | 
| maske\_rcnn\_inception\_resnet\_v2 |  |  |  |  | X |  |  |  |  |  | 
| ssd\_mobilenet\_v2 |  |  |  |  | X | X |  |  |  |  | 
| faster\_rcnn\_resnet50\_low Vorschläge |  |  |  |  | X |  |  |  |  |  | 
| rfcn\_resnet101 |  |  |  |  | X |  |  |  |  |  | 

------
#### [ TensorFlow.Keras ]


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| DenseNet121 | X | X |  | X | X | X |  | X | X | 
| DenseNet201 | X | X |  | X | X | X |  |  | X | 
| Inception V3 | 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 V2 |  |  |  | X | X | X |  |  | X | 
| ResNet152 |  |  |  | X | X |  |  |  | X | 
| ResNet152 gegen 2 |  |  |  | X | X |  |  |  | X | 
| ResNet50 | X | X |  | X | X |  |  | X | X | 
| ResNet50 V 2 | X | X |  | X | X | X |  | X | X | 
| VGG 16 |  |  |  | X | X |  |  | X | X | 
| Ausnahme | X | X |  | X | X | X |  | X | X | 

------

## TensorFlow-Lite
<a name="collapsible-section-07"></a>

------
#### [ TensorFlow-Lite (FP32) ]


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU 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 | 
| mansnet\_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) ]


| Modelle | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | ZU TDA4VM | Qualcomm QCS603 | X86\_Linux | X86\_Windows | i.MX 8M Plus | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| inception\_v1 |  |  |  |  |  |  | X |  |  | X | 
| Inception\_v2 |  |  |  |  |  |  | X |  |  | X | 
| Anfang\_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 |  |  |  | 

------