本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 支援的架構、裝置、系統和架構
<a name="neo-supported-devices-edge"></a>

Amazon SageMaker Neo 支援常見的機器學習架構、Edge 裝置、作業系統和晶片架構。請選取下列其中一個主題，深入了解 Neo 是否支援您的架構、Edge 裝置、作業系統和晶片架構。

您可以在[測試模型模型](neo-supported-edge-tested-models.md)區段中尋找 Amazon SageMaker Neo 團隊已測試的模型清單。

**注意**  
在傳送壓縮的 TAR 檔案進行編譯之前，Ambarella 裝置需要將其他檔案包含在該檔案中。如需更多詳細資訊，請參閱 [故障診斷 Ambarella 錯誤](neo-troubleshooting-target-devices-ambarella.md)。
對於 i.MX 8M Plus，TIM-VX (libtim-vx.so) 為必要。[如需如何建置 TIM-VX 的資訊，請參閱 TIM-VX GitHub 儲存庫](https://github.com/VeriSilicon/TIM-VX)。

**Topics**
+ [支援的架構](neo-supported-devices-edge-frameworks.md)
+ [支援的裝置、晶片架構和系統](neo-supported-devices-edge-devices.md)
+ [測試模型模型](neo-supported-edge-tested-models.md)

# 支援的架構
<a name="neo-supported-devices-edge-frameworks"></a>

Amazon SageMaker 支援以下架構。


| 架構 | 框架版本 | 模型版本 | 模型 | 模型格式 (以 \$1.tar.gz 封裝) | 工具組 | 
| --- | --- | --- | --- | --- | --- | 
| MXNet | 1.8 | 支援 1.8 或更早版本 | 影像分類、物件偵測、語意分割、姿勢估算、活動辨識 | 一個符號檔案 (.json) 和一個參數檔案 (.params) | GluonCV v0.8.0 | 
| ONNX | 1.7 | 支援 1.7 或更早版本 | 影像影像分類、SVM | 一個模型檔案 (.onnx) |  | 
| Keras | 2.2 | 支援 2.2 或更早版本 | 影像分類 | 一個模型定義檔案 (.h5) |  | 
| PyTorch | 1.7、1.8 | 支援 1.7、1.8 或更早版本 | 影像分類、物件偵測 | 一個模型定義檔案 (.pth) |  | 
| TensorFlow | 1.15、2.4、2.5 (僅適用於 ml.inf1.\$1 執行個體) | 支援 1.15、2.4、2.5 (僅適用於 ml.inf1 執行個體) 或更早版本 | 影像分類、物件偵測 | \$1對於儲存模型，一個 .pb 或一個 .pbtxt 檔案和一個包含變數的變數目錄 \$1對於凍結的模型，只有一個 .pb 或 .pbtxt 檔案 |  | 
| TensorFlow 精簡版 | 1.15 | 支援 1.15 或更早版本 | 影像分類、物件偵測 | 一個模型定義 Flatbuffer 檔案 (.tflite) |  | 
| XGBoost | 1.3 | 支援 1.3 或更早版本 | 決策樹 | 一個 XGBoost 模型檔案 (模型)，其中樹中的節點數量低於 2^31 |  | 
| DARKNET |  |  | 影像分類、物件偵測 (不支援 Yolo 模型) | 一個組態 (.cfg) 檔案和一個權重 (.weights) 檔案 |  | 

# 支援的裝置、晶片架構和系統
<a name="neo-supported-devices-edge-devices"></a>

Amazon SageMaker Neo 支援下列裝置、晶片架構和作業系統。

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

您可以使用 [Amazon SageMaker AI 主控台](https://console.aws.amazon.com/sagemaker)中的下拉式清單選取裝置，或透過指定 [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html) API 輸出組態中的 `TargetDevice` 選取裝置。

您可以選擇下列其中一個 Edge 裝置：


| 裝置清單 | 單晶片系統 (SoC) | 作業系統 | 架構 | 加速器 | 編譯器選項範例 | 
| --- | --- | --- | --- | --- | --- | 
| aisage | 無 | Linux | ARM64 | 馬利 | 無 | 
| amba\$1cv2 | CV2 | Arch Linux | ARM64 | cvflow | 無 | 
| amba\$1cv22 | CV22 | Arch Linux | ARM64 | cvflow | 無 | 
| amba\$1cv25 | CV25 | Arch Linux | ARM64 | cvflow | 無 | 
| coreml | 無 | iOS、macOS | 無 | 無 | \$1"class\$1labels": "imagenet\$1labels\$11000.txt"\$1 | 
| imx8qm | NXP imx8 | Linux | ARM64 | 無 | 無 | 
| imx8mplus | i.MX 8M Plus | Linux | ARM64 | NPU | 無 | 
| jacinto\$1tda4vm | TDA4VM | Linux | ARM | TDA4VM | 無 | 
| jetson\$1nano | 無 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$153', 'trt-ver': '5.0.6', 'cuda-ver': '10.0'\$1對於 `TensorFlow2`，`{'JETPACK_VERSION': '4.6', 'gpu_code': 'sm_72'}` | 
| jetson\$1tx1 | 無 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$153', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'\$1 | 
| jetson\$1tx2 | 無 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$162', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'\$1 | 
| jetson\$1xavier | 無 | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$172', 'trt-ver': '5.1.6', 'cuda-ver': '10.0'\$1 | 
| qcs605 | 無 | Android | ARM64 | 馬利 | \$1'ANDROID\$1PLATFORM': 27\$1 | 
| qcs603 | 無 | Android | ARM64 | 馬利 | \$1'ANDROID\$1PLATFORM': 27\$1 | 
| rasp3b | ARM A56 | Linux | ARM\$1EABIHF | 無 | \$1'mattr': ['\$1neon']\$1 | 
| rasp4b | ARM A72 | 無 | 無 | 無 | 無 | 
| rk3288 | 無 | Linux | ARM\$1EABIHF | 馬利 | 無 | 
| rk3399 | 無 | Linux | ARM64 | 馬利 | 無 | 
| sbe\$1c | 無 | Linux | x86\$164 | 無 | \$1'mcpu': 'core-avx2'\$1 | 
| sitara\$1am57x | AM57X | Linux | ARM64 | EVE 和/或 C66x DSP | 無 | 
| x86\$1win32 | 無 | Windows 10 | X86\$132 | 無 | 無 | 
| x86\$1win64 | 無 | Windows 10 | X86\$132 | 無 | 無 | 

如需每個目標裝置的 JSON 鍵值編譯器選項的詳細資訊，請參閱 [`OutputConfig` API](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html) 資料類型中的 `CompilerOptions` 欄位。

## 系統與晶片架構
<a name="neo-supported-edge-granular"></a>

下列查詢資料表提供有關 Neo 模型編譯任務之可用作業系統和架構的資訊。

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


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

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


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

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


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

------

# 測試模型模型
<a name="neo-supported-edge-tested-models"></a>

下列可摺疊區段提供 Amazon SageMaker Neo 團隊所測試之機器學習模型的資訊。根據您的架構展開可折疊區段，檢查模型是否受測試。

**注意**  
可使用 Neo 進行編譯之模型的清單並不完整。

請參閱[支援的架構](neo-supported-devices-edge-frameworks.md)和 [SageMaker AI Neo 支援的運算子](https://aws.amazon.com/releasenotes/sagemaker-neo-supported-frameworks-and-operators/)，深入了解您是否可以使用 SageMaker Neo 編譯模型。

## 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 精簡版
<a name="collapsible-section-07"></a>

------
#### [ 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 |  |  |  | 

------