IoTKITs: A novel dataset for IoT education kit recognitionMendeley Data
This paper introduces IoTKITs, a novel and well-annotated dataset specifically designed for the identification and classification of IoT education kits (KITs), addressing the scarcity of publicly available datasets in this domain. The dataset comprises over 3,000 high-resolution images of various KI...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925003804 |
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| Summary: | This paper introduces IoTKITs, a novel and well-annotated dataset specifically designed for the identification and classification of IoT education kits (KITs), addressing the scarcity of publicly available datasets in this domain. The dataset comprises over 3,000 high-resolution images of various KITs, including popular designs such as Arduino Uno, Arduino Nano, ESP32, and others, with detailed annotations for object detection tasks. To establish baselines, we evaluated state-of-the-art object detection models, including YOLOv5, YOLOv7, Faster R-CNN, and SSD, on the dataset. IoTKITs is designed to advance KIT classification research and foster applications in education, embedded systems, and smart learning environments. |
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| ISSN: | 2352-3409 |