本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

# 支持的框架、设备、系统和架构
<a name="neo-supported-devices-edge"></a>

Amazon SageMaker Neo 支持常见的机器学习框架、边缘设备、操作系统和芯片架构。选择以下主题之一，了解 Neo 是否支持您的框架、边缘设备、操作系统和芯片架构。

您可以在 [经过测试的模型](neo-supported-edge-tested-models.md)部分中找到经 Amazon SageMaker Neo 团队测试过的型号列表。

**注意**  
Ambarella 设备需要在压缩的 TAR 文件中包含其他文件，然后才能将其发送出去以进行编译。有关更多信息，请参阅 [排查 Ambarella 错误](neo-troubleshooting-target-devices-ambarella.md)。
i.MX 8M Plus 需要 TIM-VX (libtim-vx.so)。有关如何构建 TIM-VX 的信息，请参阅 [TIM-VX GitHub 存储库](https://github.com/VeriSilicon/TIM-VX)。

**Topics**
+ [支持的框架](neo-supported-devices-edge-frameworks.md)
+ [支持的设备、芯片架构和系统](neo-supported-devices-edge-devices.md)
+ [经过测试的模型](neo-supported-edge-tested-models.md)

# 支持的框架
<a name="neo-supported-devices-edge-frameworks"></a>

Amazon SageMaker Neo 支持以下框架。


| 框架 | 框架版本 | 模型版本 |   模型 | 模型格式（打包为 \$1.tar.gz） | 工具包 | 
| --- | --- | --- | --- | --- | --- | 
| MXNet | 1.8 | 支持 1.8 或更早版本 | 图像分类、对象检测、语义分割、姿态估算、活动识别 | 一个符号文件 (.json) 和一个参数文件 (.params) | GluonCV v0.8.0 | 
| ONNX | 1.7 | 支持 1.7 或更早版本 | 图像分类、SVM | 一个模型文件 (.onnx) |  | 
| Keras | 2.2 | 支持 2.2 或更早版本 | 图像分类 | 一个模型定义文件 (.h5) |  | 
| PyTorch | 1.7、1.8 | 支持 1.7、1.8 或更早版本 | 图像分类、对象检测 | 一个模型定义文件 (.pth) |  | 
| TensorFlow | 1.15、2.4、2.5（仅适用于 ml.inf1.\$1 实例） | 支持 1.15、2.4、2.5（仅适用于 ml.inf1.\$1 实例）或更早版本 | 图像分类、对象检测 | \$1对于保存的模型，需要一个.pb 或一个.pbtxt 文件以及一个包含变量的变量目录 \$1对于冻结的模型，只需要一个.pb 或.pbtxt 文件 |  | 
| TensorFlow-Lite | 1.15 | 支持 1.15 或更早版本 | 图像分类、对象检测 | 一个模型定义 flatbuffer 文件 (.tflite) |  | 
| XGBoost | 1.3 | 支持 1.3 或更早版本 | 决策树 | 一个 XGBoost 模型文件 (.model)，其中树的节点数小于 2^31 |  | 
| DARKNET |  |  | 图像分类、对象检测（不支持 Yolo 模型） | 一个配置 (.cfg) 文件和一个权重 (.weights) 文件 |  | 

# 支持的设备、芯片架构和系统
<a name="neo-supported-devices-edge-devices"></a>

Amazon SageMaker Neo 支持以下设备、芯片架构和操作系统。

## 设备
<a name="neo-supported-edge-devices"></a>

您可以使用 [Amazon SageMaker AI 控制台](https://console.aws.amazon.com/sagemaker)中的下拉列表选择设备，或者在 [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html) API 的输出配置中指定 `TargetDevice`。

您可以选择下列边缘设备之一：


| 设备列表 | 片上系统 (SoC) | 操作系统 | 架构 | Accelerator | 编译器选项示例 | 
| --- | --- | --- | --- | --- | --- | 
| aisage | 无 | Linux | ARM64 | Mali | 无 | 
| amba\$1cv2 | CV2 | Arch Linux | ARM64 | cvflow | 无 | 
| amba\$1cv22 | CV22 | Arch Linux | ARM64 | cvflow | 无 | 
| amba\$1cv25 | CV25 | Arch Linux | ARM64 | cvflow | 无 | 
| coreml | 无 | iOS、macOS | 无 | 无 | \$1"class\$1labels": "imagenet\$1labels\$11000.txt"\$1 | 
| imx8qm | NXP imx8 | Linux | ARM64 | 无 | 无 | 
| imx8mplus | i.MX 8M Plus | Linux | ARM64 | NPU | 无 | 
| jacinto\$1tda4vm | TDA4VM | Linux | ARM | TDA4VM | 无 | 
| jetson\$1nano | 无 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$153', 'trt-ver': '5.0.6', 'cuda-ver': '10.0'\$1适用于 `TensorFlow2`、`{'JETPACK_VERSION': '4.6', 'gpu_code': 'sm_72'}` | 
| jetson\$1tx1 | 无 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$153', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'\$1 | 
| jetson\$1tx2 | 无 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$162', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'\$1 | 
| jetson\$1xavier | 无 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$172', 'trt-ver': '5.1.6', 'cuda-ver': '10.0'\$1 | 
| qcs605 | 无 | Android | ARM64 | Mali | \$1'ANDROID\$1PLATFORM': 27\$1 | 
| qcs603 | 无 | Android | ARM64 | Mali | \$1'ANDROID\$1PLATFORM': 27\$1 | 
| rasp3b | ARM A56 | Linux | ARM\$1EABIHF | 无 | \$1'mattr': ['\$1neon']\$1 | 
| rasp4b | ARM A72 | 无 | 无 | 无 | 无 | 
| rk3288 | 无 | Linux | ARM\$1EABIHF | Mali | 无 | 
| rk3399 | 无 | Linux | ARM64 | Mali | 无 | 
| sbe\$1c | 无 | Linux | x86\$164 | 无 | \$1'mcpu': 'core-avx2'\$1 | 
| sitara\$1am57x | AM57X | Linux | ARM64 | EVE 和/或 C66x DSP | 无 | 
| x86\$1win32 | 无 | Windows 10 | X86\$132 | 无 | 无 | 
| x86\$1win64 | 无 | Windows 10 | X86\$132 | 无 | 无 | 

有关每台目标设备的 JSON 键值编译器选项的更多信息，请参阅 [`OutputConfig`API](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html) 数据类型中的 `CompilerOptions` 字段。

## 系统和芯片架构
<a name="neo-supported-edge-granular"></a>

以下查找表提供有关 Neo 模型编译作业的可用操作系统和架构的信息。

------
#### [ Linux ]


| Accelerator | X86\$164 | X86 | ARM64 | ARM\$1EABIHF | ARM\$1EABI | 
| --- | --- | --- | --- | --- | --- | 
| 没有加速器 (CPU) | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | 
| Nvidia GPU | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | 
| Intel\$1Graphics | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | 
| ARM Mali | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | 

------
#### [ Android ]


| Accelerator | X86\$164 | X86 | ARM64 | ARM\$1EABIHF | ARM\$1EABI | 
| --- | --- | --- | --- | --- | --- | 
| 没有加速器 (CPU) | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | 
| Nvidia GPU | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | 
| Intel\$1Graphics | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | 
| ARM Mali | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | 

------
#### [ Windows ]


| Accelerator | X86\$164 | X86 | ARM64 | ARM\$1EABIHF | ARM\$1EABI | 
| --- | --- | --- | --- | --- | --- | 
| 没有加速器 (CPU) | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/success_icon.svg) 是 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | ![\[alt text not found\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/negative_icon.svg) 否 | 

------

# 经过测试的模型
<a name="neo-supported-edge-tested-models"></a>

以下可折叠部分提供了有关经过 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。

查看 [支持的框架](neo-supported-devices-edge-frameworks.md)和 [SageMaker Neo AI 支持的运算符](https://aws.amazon.com/releasenotes/sagemaker-neo-supported-frameworks-and-operators/)，了解您是否可以使用 SageMaker Neo 编译模型。

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


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| Alexnet |  |  |  |  |  |  |  |  |  | 
| Resnet50 | X | X |  | X | X | X |  | X | X | 
| YOLOv2 |  |  |  | X | X | X |  | X | X | 
| YOLOv2\$1tiny | X | X |  | X | X | X |  | X | X | 
| YOLOv3\$1416 |  |  |  | X | X | X |  | X | X | 
| YOLOv3\$1tiny | X | X |  | X | X | X |  | X | X | 

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


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| 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\$10.5 | X | X |  | X | X | X |  |  | X | 
| MobileNetV2\$11.0 | X | X | X | X | X | X | X | X | X | 
| MobileNetV3\$1Large | X | X | X | X | X | X | X | X | X | 
| MobileNetV3\$1Small | X | X | X | X | X | X | X | X | X | 
| ResNeSt50 |  |  |  | X | X |  |  | X | X | 
| ResNet18\$1v1 | X | X | X | X | X | X |  |  | X | 
| ResNet18\$1v2 | X | X |  | X | X | X |  |  | X | 
| ResNet50\$1v1 | X | X | X | X | X | X |  | X | X | 
| ResNet50\$1v2 | X | X | X | X | X | X |  | X | X | 
| ResNext101\$132x4d |  |  |  |  |  |  |  |  |  | 
| ResNext50\$132x4d | X |  | X | X | X |  |  | X | X | 
| SENet\$1154 |  |  |  | X | X | X |  | X | X | 
| SE\$1ResNext50\$132x4d | 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\$1v1b\$10.89 | X | X |  | X | X | X |  |  | X | 
| resnet50\$1v1d\$10.11 | X | X |  | X | X | X |  |  | X | 
| resnet50\$1v1d\$10.86 | X | X | X | X | X | X |  | X | X | 
| ssd\$1512\$1mobilenet1.0\$1coco | X |  | X | X | X | X |  | X | X | 
| ssd\$1512\$1mobilenet1.0\$1voc | X |  | X | X | X | X |  | X | X | 
| ssd\$1resnet50\$1v1 | X |  | X | X | X |  |  | X | X | 
| yolo3\$1darknet53\$1coco | X |  |  | X | X |  |  | X | X | 
| yolo3\$1mobilenet1.0\$1coco | X | X |  | X | X | X |  | X | X | 
| deeplab\$1resnet50 |  |  | X |  |  |  |  |  |  | 

## Keras
<a name="collapsible-section-03"></a>


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| densenet121 | X | X | X | X | X | X |  | X | X | 
| densenet201 | X | X | X | X | X | X |  |  | X | 
| inception\$1v3 | X | X |  | X | X | X |  | X | X | 
| mobilenet\$1v1 | X | X | X | X | X | X |  | X | X | 
| mobilenet\$1v2 | X | X | X | X | X | X |  | X | X | 
| resnet152\$1v1 |  |  |  | X | X |  |  |  | X | 
| resnet152\$1v2 |  |  |  | X | X |  |  |  | X | 
| resnet50\$1v1 | X | X | X | X | X |  |  | X | X | 
| resnet50\$1v2 | X | X | X | X | X | X |  | X | X | 
| vgg16 |  |  | X | X | X |  |  | X | X | 

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


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| 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)
<a name="collapsible-section-05"></a>


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Ambarella CV25 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| densenet121 | X | X | X | X | X | X | X |  | X | X | 
| inception\$1v3 |  | 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\$1resnet50\$1fpn |  |  |  |  | X | X |  |  |  |  | 
| maskrcnn\$1resnet50\$1fpn |  |  |  |  | X | X |  |  |  |  | 

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

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


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Ambarella CV25 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| densenet201 | X | X | X | X | X | X | X |  | X | X | 
| inception\$1v3 | X | X | X |  | X | X | X |  | X | X | 
| mobilenet100\$1v1 | X | X | X |  | X | X | X |  |  | X | 
| mobilenet100\$1v2.0 | X | X | X |  | X | X | X |  | X | X | 
| mobilenet130\$1v2 | X | X |  |  | X | X | X |  |  | X | 
| mobilenet140\$1v2 | X | X | X |  | X | X | X |  | X | X | 
| resnet50\$1v1.5 | X | X |  |  | X | X | X |  | X | X | 
| resnet50\$1v2 | X | X | X | X | X | X | X |  | X | X | 
| squeezenet | X | X | X | X | X | X | X |  | X | X | 
| mask\$1rcnn\$1inception\$1resnet\$1v2 |  |  |  |  | X |  |  |  |  |  | 
| ssd\$1mobilenet\$1v2 |  |  |  |  | X | X |  |  |  |  | 
| faster\$1rcnn\$1resnet50\$1lowproposals |  |  |  |  | X |  |  |  |  |  | 
| rfcn\$1resnet101 |  |  |  |  | X |  |  |  |  |  | 

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


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| 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 | 

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

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


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | i.MX 8M Plus | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| densenet\$12018\$104\$127 | X |  |  | X | X | X |  |  | X |  | 
| inception\$1resnet\$1v2\$12018\$104\$127 |  |  |  | X | X | X |  |  | X |  | 
| inception\$1v3\$12018\$104\$127 |  |  |  | X | X | X |  |  | X | X | 
| inception\$1v4\$12018\$104\$127 |  |  |  | X | X | X |  |  | X | X | 
| mnasnet\$10.5\$1224\$109\$107\$12018 | X |  |  | X | X | X |  |  | X |  | 
| mnasnet\$11.0\$1224\$109\$107\$12018 | X |  |  | X | X | X |  |  | X |  | 
| mnasnet\$11.3\$1224\$109\$107\$12018 | X |  |  | X | X | X |  |  | X |  | 
| mobilenet\$1v1\$10.25\$1128 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$10.25\$1224 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$10.5\$1128 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$10.5\$1224 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$10.75\$1128 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$10.75\$1224 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$11.0\$1128 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v1\$11.0\$1192 | X |  |  | X | X | X |  |  | X | X | 
| mobilenet\$1v2\$11.0\$1224 | X |  |  | X | X | X |  |  | X | X | 
| resnet\$1v2\$1101 |  |  |  | X | X | X |  |  | X |  | 
| squeezenet\$12018\$104\$127 | X |  |  | X | X | X |  |  | X |  | 

------
#### [ TensorFlow-Lite (INT8) ]


|   模型 | ARM V8 | ARM Mali | Ambarella CV22 | Nvidia | Panorama | TI TDA4VM | Qualcomm QCS603 | X86\$1Linux | X86\$1Windows | i.MX 8M Plus | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| inception\$1v1 |  |  |  |  |  |  | X |  |  | X | 
| inception\$1v2 |  |  |  |  |  |  | X |  |  | X | 
| inception\$1v3 | X |  |  |  |  | X | X |  | X | X | 
| inception\$1v4\$1299 | X |  |  |  |  | X | X |  | X | X | 
| mobilenet\$1v1\$10.25\$1128 | X |  |  |  |  | X |  |  | X | X | 
| mobilenet\$1v1\$10.25\$1224 | X |  |  |  |  | X |  |  | X | X | 
| mobilenet\$1v1\$10.5\$1128 | X |  |  |  |  | X |  |  | X | X | 
| mobilenet\$1v1\$10.5\$1224 | X |  |  |  |  | X |  |  | X | X | 
| mobilenet\$1v1\$10.75\$1128 | X |  |  |  |  | X |  |  | X | X | 
| mobilenet\$1v1\$10.75\$1224 | X |  |  |  |  | X | X |  | X | X | 
| mobilenet\$1v1\$11.0\$1128 | X |  |  |  |  | X |  |  | X | X | 
| mobilenet\$1v1\$11.0\$1224 | X |  |  |  |  | X | X |  | X | X | 
| mobilenet\$1v2\$11.0\$1224 | X |  |  |  |  | X | X |  | X | X | 
| deeplab-v3\$1513 |  |  |  |  |  |  | X |  |  |  | 

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