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| Table of Contents | ||||
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Preparation
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Please refer here for the environment construction procedure.
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AI model convert tool: Tensorflow
AI model convert: ONNX(PyTorch)
Evaluate AI model
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The CV tool does not include a mechanism to evaluate the inference time, accuracy, etc. of the converted model.
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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.
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First, copy the DnnSdApp package (DnnSdApp_V0_45_ambaCV2XambaCV2X5X.ext) in the container to the host PC.
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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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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
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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
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