# Supported Frameworks, Devices, Systems, and Architectures


Amazon SageMaker Neo supports common machine learning frameworks, edge devices, operating systems, and chip architectures. Find out if Neo supports your framework, edge device, OS, and chip architecture by selecting one of the topics below.

You can find a list of models that have been tested by the Amazon SageMaker Neo Team in the [Tested Models](neo-supported-edge-tested-models.md) section.

**Note**  
Ambarella devices require additional files to be included within the compressed TAR file before it is sent for compilation. For more information, see [Troubleshoot Ambarella Errors](neo-troubleshooting-target-devices-ambarella.md).
TIM-VX (libtim-vx.so) is required for i.MX 8M Plus. For information on how to build TIM-VX, see the [TIM-VX GitHub repository](https://github.com/VeriSilicon/TIM-VX).

**Topics**
+ [

# Supported Frameworks
](neo-supported-devices-edge-frameworks.md)
+ [

# Supported Devices, Chip Architectures, and Systems
](neo-supported-devices-edge-devices.md)
+ [

# Tested Models
](neo-supported-edge-tested-models.md)

# Supported Frameworks


Amazon SageMaker Neo supports the following frameworks. 


| Framework | Framework Version | Model Version | Models | Model Formats (packaged in \$1.tar.gz) | Toolkits | 
| --- | --- | --- | --- | --- | --- | 
| MXNet | 1.8 | Supports 1.8 or earlier | Image Classification, Object Detection, Semantic Segmentation, Pose Estimation, Activity Recognition | One symbol file (.json) and one parameter file (.params) | GluonCV v0.8.0 | 
| ONNX | 1.7 | Supports 1.7 or earlier | Image Classification, SVM | One model file (.onnx) |  | 
| Keras | 2.2 | Supports 2.2 or earlier | Image Classification | One model definition file (.h5) |  | 
| PyTorch | 1.7, 1.8 | Supports 1.7, 1.8 or earlier | Image Classification, Object Detection | One model definition file (.pth) |  | 
| TensorFlow | 1.15, 2.4, 2.5 (only for ml.inf1.\$1 instances) | Supports 1.15, 2.4, 2.5 (only for ml.inf1.\$1 instances) or earlier | Image Classification, Object Detection | \$1For saved models, one .pb or one .pbtxt file and a variables directory that contains variables \$1For frozen models, only one .pb or .pbtxt file |  | 
| TensorFlow-Lite | 1.15 | Supports 1.15 or earlier | Image Classification, Object Detection | One model definition flatbuffer file (.tflite) |  | 
| XGBoost | 1.3 | Supports 1.3 or earlier | Decision Trees | One XGBoost model file (.model) where the number of nodes in a tree is less than 2^31 |  | 
| DARKNET |  |  | Image Classification, Object Detection (Yolo model is not supported) | One config (.cfg) file and one weights (.weights) file |  | 

# Supported Devices, Chip Architectures, and Systems


Amazon SageMaker Neo supports the following devices, chip architectures, and operating systems.

## Devices


You can select a device using the dropdown list in the [Amazon SageMaker AI console](https://console.aws.amazon.com/sagemaker) or by specifying the `TargetDevice` in the output configuration of the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateCompilationJob.html) API.

You can choose from one of the following edge devices: 


| Device List | System on a Chip (SoC) | Operating System | Architecture | Accelerator | Compiler Options Example | 
| --- | --- | --- | --- | --- | --- | 
| aisage | None | Linux | ARM64 | Mali | None | 
| amba\$1cv2 | CV2 | Arch Linux | ARM64 | cvflow | None | 
| amba\$1cv22 | CV22 | Arch Linux | ARM64 | cvflow | None | 
| amba\$1cv25 | CV25 | Arch Linux | ARM64 | cvflow | None | 
| coreml | None | iOS, macOS | None | None | \$1"class\$1labels": "imagenet\$1labels\$11000.txt"\$1 | 
| imx8qm | NXP imx8 | Linux | ARM64 | None | None | 
| imx8mplus | i.MX 8M Plus | Linux | ARM64 | NPU | None | 
| jacinto\$1tda4vm | TDA4VM | Linux | ARM | TDA4VM | None | 
| jetson\$1nano | None | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$153', 'trt-ver': '5.0.6', 'cuda-ver': '10.0'\$1For `TensorFlow2`, `{'JETPACK_VERSION': '4.6', 'gpu_code': 'sm_72'}` | 
| jetson\$1tx1 | None | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$153', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'\$1 | 
| jetson\$1tx2 | None | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$162', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'\$1 | 
| jetson\$1xavier | None | Linux | ARM64 | NVIDIA | \$1'gpu-code': 'sm\$172', 'trt-ver': '5.1.6', 'cuda-ver': '10.0'\$1 | 
| qcs605 | None | Android | ARM64 | Mali | \$1'ANDROID\$1PLATFORM': 27\$1 | 
| qcs603 | None | Android | ARM64 | Mali | \$1'ANDROID\$1PLATFORM': 27\$1 | 
| rasp3b | ARM A56 | Linux | ARM\$1EABIHF | None | \$1'mattr': ['\$1neon']\$1 | 
| rasp4b | ARM A72 | None | None | None | None | 
| rk3288 | None | Linux | ARM\$1EABIHF | Mali | None | 
| rk3399 | None | Linux | ARM64 | Mali | None | 
| sbe\$1c | None | Linux | x86\$164 | None | \$1'mcpu': 'core-avx2'\$1 | 
| sitara\$1am57x | AM57X | Linux | ARM64 | EVE and/or C66x DSP | None | 
| x86\$1win32 | None | Windows 10 | X86\$132 | None | None | 
| x86\$1win64 | None | Windows 10 | X86\$132 | None | None | 

For more information about JSON key-value compiler options for each target device, see the `CompilerOptions` field in the [`OutputConfig` API](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html) data type.

## Systems and Chip Architectures


The following look-up tables provide information regarding available operating systems and architectures for Neo model compilation jobs. 

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


| Accelerator | X86\$164 | X86 | ARM64 | ARM\$1EABIHF | ARM\$1EABI | 
| --- | --- | --- | --- | --- | --- | 
| No accelerator (CPU) | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | 
| Nvidia GPU | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | 
| Intel\$1Graphics | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | 
| ARM Mali | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | 

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


| Accelerator | X86\$164 | X86 | ARM64 | ARM\$1EABIHF | ARM\$1EABI | 
| --- | --- | --- | --- | --- | --- | 
| No accelerator (CPU) | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | 
| Nvidia GPU | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | 
| Intel\$1Graphics | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | 
| ARM Mali | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | 

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


| Accelerator | X86\$164 | X86 | ARM64 | ARM\$1EABIHF | ARM\$1EABI | 
| --- | --- | --- | --- | --- | --- | 
| No accelerator (CPU) | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/success_icon.svg) Yes | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | ![\[alt text not found\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/negative_icon.svg) No | 

------

# 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](neo-supported-devices-edge-frameworks.md) and [SageMaker AI Neo Supported Operators](https://aws.amazon.com/releasenotes/sagemaker-neo-supported-frameworks-and-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\$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



| Models | 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



| Models | 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



| Models | 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)



| Models | 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


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


| Models | 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 ]


| Models | 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 | 

------

## TensorFlow-Lite


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


| Models | 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) ]


| Models | 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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