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Table of contents
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環境構築
環境構築手順はこちらをご参照ください。
モデルの変換
モデル変換については下記ページをご参照ください。
設定ファイル仕様・サンプルモデル変換(Tensorflow編)
設定ファイル仕様・サンプルモデル変換(ONNX/PyTorch編)
モデルの評価
CVツールには、変換後のモデルの推論時間、精度などを評価する仕組みは含まれていません。
変換後モデルをi-PROカメラ上で実行し、評価を行ってください。
推論時間
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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.
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.
Run the converted model on the i-PRO camera and evaluate it.
Inference time
Measure the time before and after the inference execution API Adam_AI_RunNet() 呼び出し前後の時間を計測してくださいcall
精度
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precision
Evaluate the accuracy using the data obtained by the API / Adam_AI_GetOutput () で取得したデータを使って、精度評価を行ってください。
CVツールには、変換後モデルの推論時間と出力層のデータを取得できるサンプルアプリが含まれています。
次から使用方法を紹介します。
評価の準備
評価にはChrome拡張機能のADAM OPERATION UIを使用します。
インストール方法の詳細はSDK同梱のドキュメント「AdamAppDevelopmentManualForIpro.pdf」を参照してください。
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SDK v1.70以降のADAM OPERATION UIの手順を記載しています。 |
また、アプリがインストールできるi-PROネットワークカメラを用意してください。
対象品番:i-PROカメラへのソフトウェアインストール条件 that acquires the data of the output layer of the model.
The CV tool includes a sample app that allows you to obtain inference time and output layer data for the converted model.
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.
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
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Install the app
First, copy the DnnSdApp package (DnnSdApp_V0_4_ambaCV2X.ext) を、ホストPCにコピーします。in the container to the host PC.
[Work Directory]
は任意のディレクトリAny directory
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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_4_ambaCV2X.ext /work |
Launch a browser and access the detailed setting
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Set [Basic] - [SD memory card] - [Operation Mode] - [SD memory card] and [Ext. software mode] to "On".
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If you do not want to use the SD card, please select "Not use". |
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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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layernamein: Input layer name
layernameout: Output layer name (separated by comma for multiple settings)
NETNAME: Model name
TftpServerIP:TFTP server address where models are stored
*Set if SD card is not used
Prepare evaluation images
Compress the images to be used for evaluation (dnn.tar.gz) .
Follow the folder structure below, either jpeg or mp4 only can be used.
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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Info |
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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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Info |
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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.
Please replace the folder name with "SSD" ⇒ "DNN".