GstInference - Supported backends - NCSDK

GstInference
Introduction
Getting started
Getting the code Building the plugin Quick Starting Guide
Supported architectures
InceptionV1 InceptionV2 InceptionV3 InceptionV4 TinyYoloV2 TinyYoloV3 YoloV2 ResNetV1 MobileNetV2 MobileNetV2 + SSD AlexNet
Supported backends
TensorFlow Tensorflow-Lite Coral from Google Caffe TensorRT ONNXRT ONNXRT ACL ONNXRT OpenVINO
Metadata and Signals
GstClassificationMeta GstInferenceMeta Signals
Overlay Elements
Inference Overlay
Utils Elements
InferenceFilter InferenceCrop InferenceDebug InferenceBin
Legacy pipelines
PC NANO TX2 Xavier IMX8
Example pipelines
Example applications
Classification Detection Hand Pose Recognition
Benchmarks
Model Zoo
Project Status
Releases Roadmap
Contact Us

The NCSDK Intel® Movidius™ Neural Compute SDK (Intel® Movidius™ NCSDK) enables deployment of deep neural networks on compatible devices such as the Intel® Movidius™ Neural Compute Stick. The NCSDK includes a set of software tools to compile, profile, and validate DNNs (Deep Neural Networks) as well as APIs on C/C++ and Python for application development.

The NCSDK has two general usages:

• Profiling, tuning, and compiling a DNN models.
• Prototyping user applications, that run accelerated with a neural compute device hardware, using the NCAPI.

Installation

You can install the NCSDK on a system running Linux directly, downloading a Docker container, on a virtual machine or using a Python virtual environment. Al the possible installation paths are documented on the official installation guide.

Tools

mvNCCheck

Checks the validity of a Caffe or TensorFlow model on a neural compute device. The check is done by running an inference on both the device and in software and then comparing the results to determine a if the network passes or fails. This tool works best with image classification networks. You can check all the available options on the official documentation.

For example lets test the googlenet caffe model downloaded by the ncappzoo repo:

mvNCCheck -w bvlc_googlenet.caffemodel -i ../../data/images/nps_electric_guitar.png -s 12 -id 546  deploy.prototxt -S 255 -M 110

• -w indicates the weights file
• -i the input image
• -s the number of shaves
• -id the expected label id for the input image (you can find the id for any imagenet model here)
• -S is the scaling sice
• -M is the substracted mean after scaling

Most of these parameters are available from the model documentation. The command produces the following result:

lob generated
USB: Transferring Data...
USB: Myriad Execution Finished
USB: Myriad Connection Closing.
USB: Myriad Connection Closed.
Result:  (1000,)
1) 546 0.99609
2) 402 0.0038853
3) 420 8.9228e-05
4) 327 0.0
5) 339 0.0
Expected:  (1000,)
1) 546 0.99609
2) 402 0.0039177
3) 420 9.0837e-05
4) 889 1.2875e-05
5) 486 5.3644e-06
------------------------------------------------------------
 Obtained values 
------------------------------------------------------------
 Obtained Min Pixel Accuracy: 0.0032552085031056777% (max allowed=2%), Pass
 Obtained Average Pixel Accuracy: 7.264380030846951e-06% (max allowed=1%), Pass
 Obtained Percentage of wrong values: 0.0% (max allowed=0%), Pass
 Obtained Pixel-wise L2 error: 0.00011369892179413199% (max allowed=1%), Pass
 Obtained Global Sum Difference: 7.236003875732422e-05
------------------------------------------------------------

mvNCCompile

Compiles a network and weights files from Caffe or TensorFlow models into a graph file that is compatible with the NCAPI.

For example, giving a caffe model (bvlc_googlenet.caffemodel) and a network description (deploy.prototxt):

mvNCCompile -w bvlc_googlenet.caffemodel -s 12 deploy.prototxt

This command will output the graph and output_expected.npy files, that will be used later on the API