Table of contents
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Preparation
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Please refer here for the environment construction procedure.
Conversion
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Please refer to the following page for model conversion.
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AI model convert tool: Tensorflow
AI model convert: ONNX(PyTorch)
Evaluate AI model
The CV tool does not include a mechanism to evaluate the inference time, accuracy, etc. of the converted model.
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Run Please evaluate in one of the following ways.
Running the converted model on the i-PRO camera
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Inference time
Measure the time before and after the inference execution API (Adam_AI_RunNet()) call
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Precision
Evaluate the accuracy using the data obtained by the API
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(Adam_AI_GetOutput()) that acquires the data of the output layer of the model.
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Using sample app
The CV tool includes a sample app that allows you to obtain inference time and output layer data for the converted model. This page explains how to use it.Using simulator in CV tool
It is possible to perform inference for the converted model. Please refer here on how to use it.
Sample app for evaluation
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Preparing for the evaluation
Use the Chrome extension's ADAM OPERATION UI for evaluation.
For details on how to install, refer to the document "AdamAppDevelopmentManualForIpro.pdf" included with the SDK. here.
Also, make sure you have an i-PRO network camera that the app can install on.
Info |
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Product Number: Installation conditions for applications - FAQ - Development Partner Portal (En) (i-pro.com) |
DnnSdApp
Install the app
First, copy the DnnSdApp package (DnnSdApp_V0_45_ambaCV2XambaCV2X5X.ext) in the container to the host PC.
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Code Block |
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$ cd [Work Directory] $ sudo docker run -it --rm -v $(pwd):/work [image name] /bin/bash $ cp /home/cvtool/app/DnnSdApp_V0_45_ambaCV2XambaCV2X5X.ext /work |
Launch a browser and access the detailed setting
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Move to the Ext. software and install DnnSdApp.
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Change settings to match your rating model
Configure various settings with ADAM OPERATION UI.
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layernameout: Output layer name (separated by comma for multiple settings)
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DnnSdApp may not work properly, when “/” is contained in layernamein or layernameout. |
NETNAME: Model name
TftpServerIP:TFTP server address where models are stored
*Set if SD card is not used
ChannelNum:Channels of model
ImgHeight:Height of input image
ImgWidth:Width of input image
PixelFormat:Pixel format of model
Prepare evaluation images
Compress the images to be used for evaluation (dnn.tar.gz) .
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Code Block |
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tar cvzf dnn.tar.gz dnn |
Folder configuration | Remarks | ||
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dnn/ | test_jpeg/ | yyy1.jpg | jpeg placement directory, file names are arbitrary File extension: ".jpg", ".jpeg", ".JPG", ".JPEG"
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yyy2.jpg | |||
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test_mp4/
| zzz1.mp4 | mp4 placement directory, file name to be deployed is arbitrary File ectension: ".mp4" | |
zzz2.mp4 | |||
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Upload images to DnnSdApp
Open the app screen and upload the image data.
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If the size of the image data (dnn .tar.gz) is larger than 70MB, place the dnn folder directly on the SD card. [SD Card]/dnn_sd_app/dnn/~~ |
Upload the model file to the app
Upload the model file.
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If you don't use an SD card, use a TFTP server to upload your model. Store the model file on a TFTP server (the same IP as the one set in AppPref), and then click the "Send" button to transfer the model to the camera. |
Run the app
After placing the model and image, click the "Start" button to start execution.
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Download the results
Once the run is complete, you can get the result file from the Download button.
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SsdSdApp
The operation is similar to DnnSdApp.
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