Prepare for training yolov3 on the self-built data set (2): Raspberry Pi automatically collects pictures, tensorflow_label_tool downloads pictures, yolo_mark annotation and cleaning data (with python script) - Code World

Prepare for training yolov3 on the self-built data set (2): Raspberry Pi automatically collects pictures, tensorflow_label_tool downloads pictures, yolo_mark annotation and cleaning data (with python script)

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Origin blog.csdn.net/sinat_35907936/article/details/89086081

Prepare for training yolov3 on the self-built data set (2): Raspberry Pi automatically collects pictures, tensorflow_label_tool downloads pictures, yolo_mark annotation and cleaning data (with python script)

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Prepare yolov3 on the self-built data set (1): Download the Open Images V4 data set quickly and accurately according to the object type (single type & multi type), save it in yolo3 annotation format and verify with yolo_mark (with python script).

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Prepare-se para treinar yolov3 no conjunto de dados auto-construído (2): o Raspberry Pi coleta automaticamente imagens, o tensorflow_label_tool baixa imagens, anotações yolo_mark e dados de limpeza (com script python)

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Bereiten Sie sich auf das Training von yolov3 für den selbst erstellten Datensatz vor (2): Raspberry Pi sammelt automatisch Bilder, tensorflow_label_tool lädt Bilder herunter, yolo_mark Annotation und Bereinigungsdaten (mit Python-Skript)

Préparez-vous à former yolov3 sur l'ensemble de données auto-construit (2): Raspberry Pi collecte automatiquement des images, tensorflow_label_tool télécharge des images, annotation yolo_mark et données de nettoyage (avec script python)

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Подготовка к обучению yolov3 по самодостаточному набору данных (2): Raspberry Pi автоматически собирает картинки, tenorflow_label_tool загружает картинки, аннотации yolo_mark и данные очистки (с помощью скрипта python)

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Prepárese para entrenar a yolov3 en el conjunto de datos de construcción propia (2): Raspberry Pi recolecta automáticamente imágenes, tensorflow_label_tool descarga imágenes, anotaciones yolo_mark y datos de limpieza (con script de Python)

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