🐦 Von der Vogel-Kamera zum eigenen KI-Modell 🤖 (clt26) ...

What this episode covers

🐦 Standard-KI-Modelle erkennen europäische Gartenvögel oft falsch – eine Kohlmeise wird zum asiatischen Fasan. Dieser Vortrag zeigt, wie aus einem Raspberry-Pi-Vogelhausmonitor ein vollständiges Open-Source-Ökosystem für Custom-ML-Training entstand. Ausgehend von einem automatischen Kamerasystem mit Cinema 4K und 120fps-Zeitlupe (https://github.com/kamera-linux/vogel-kamera-linux) entwickelte sich die Notwendigkeit, eigene Vogelarten-Classifier zu trainieren. Mit YOLOv8-basierter Extraktion von Trainingsdaten aus Videos (https://github.com/kamera-linux/vogel-model-trainer) und automatischer Qualitätskontrolle (https://github.com/kamera-linux/vogel-video-analyzer) erreichten wir 100% Validierungsgenauigkeit für acht deutsche Gartenvogelarten. 🤖 Der Vortrag demonstriert die komplette Pipeline von der Hardware-Aufnahme über Dataset-Erstellung bis zum produktiven EfficientNet-Modell – alles auf Consumer-Hardware ohne Cloud, praktische Einblicke in Herausforderungen wie SSH-Stabilität, Hintergrundentfernung und iteratives Training inklusive. Licensed to the public under http://creativecommons.org/licenses/by/4.0 about this event: https://chemnitzer.linux-tage.de/2026/de/programm/beitrag/122

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This episode was published on March 29, 2026.

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🐦 Standard-KI-Modelle erkennen europäische Gartenvögel oft falsch – eine Kohlmeise wird zum asiatischen Fasan. Dieser Vortrag zeigt, wie aus einem Raspberry-Pi-Vogelhausmonitor ein vollständiges Open-Source-Ökosystem für Custom-ML-Training...

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