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Repo for the lecture "Applied Artificial Intelligence" at the University of Applied Sciences Esslingen created by Dionysios Satikidis and Jan Seyler.

Home Page: https://github.com/MrDio/Applied-AI-Technologies

License: GNU General Public License v3.0

Python 27.34% Jupyter Notebook 72.24% C++ 0.42%

applied-ai-technologies's Introduction

Applied-AI-Technologies

Verwendung dieser Dateien:

Tensorflow Object Detection nach diesem Tutorial installieren: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#exporting-a-trained-inference-graph

Zum Trainieren: Folge dem Tutorial mit den Bildern und dem Pre-trained-model aus Drive.

Datensatz und Pre-trained-Modell auf Google Drive. Für Verwendung in Ordner Workspace_TF_Object speichern.

https://drive.google.com/open?id=1x8Ie_WagbWyF9zqU09HSae3jIYmk7I5E

https://drive.google.com/open?id=1N6RUCqljY3HkYIF_nLoBoqExjp6xGCIq

Zum direkten Abspielen Dateien speichern und Object_detection_video.py verwenden. Oder hse-aai-2019-team4.ipynb abspielen.

Für Verwendung auf Raspi:

https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_raspbian.html#install-package

Verzeichnis in Home anlegen

Dateien aus Files_to_run_on_raspi in Verzeichnis speichern

python3 object_detection_demo_ssd_async.py
-m frozen_inference_graph.xml
-i cam
-d MYRIAD
-pt 0.6

ausführen.

Genauere Details unter Vorgehensweise.docx

applied-ai-technologies's People

Contributors

mrdio avatar jrs1986 avatar

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