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license: mit |
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tags: |
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- computer-vision |
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- object-detection |
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- e-waste |
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- yolo |
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- industrial |
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- annotated-images |
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- deep-learning |
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dataset_name: ewaste-yolo-classification |
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--- |
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# E-Waste YOLO Classification Dataset |
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**Dataset Summary** |
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This dataset contains annotated images of electronic waste (e-waste) items for object detection tasks. It was curated to support training and evaluation of YOLO-based deep learning models for automated e-waste identification and classification. |
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## ☁️ Dataset Overview |
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| Feature | Details | |
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|---------|---------| |
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| **Task** | Object Detection | |
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| **Modalities** | Images, Annotations | |
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| **Annotation Format** | YOLO / COCO compatible | |
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| **License** | MIT | |
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| **Categories** | Batteries, Circuit Boards, LCDs, Resistors, Capacitors, Regulators, IoT Sensors, etc. | |
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## 📄 Paper Reference |
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If you use this dataset, please cite: |
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> Rajeev, P. A., Dharewa, V., Lakshmi, D., Vishnuvarthanan, G., Giri, J., Sathish, T., & Alrashoud, M. (2025). *Advancing e-waste classification with customizable YOLO based deep learning models.* Scientific Reports, 15, 18151. https://doi.org/10.1038/s41598-025-94772-x |
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### 📚 BibTeX |
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```bibtex |
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@article{rajeev2025advancing, |
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title={Advancing e-waste classification with customizable YOLO based deep learning models}, |
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author={Rajeev, P. Akhil and Dharewa, Vivek and Lakshmi, D. and Vishnuvarthanan, G. and Giri, J. and Sathish, T. and Alrashoud, M.}, |
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journal={Scientific Reports}, |
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volume={15}, |
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pages={18151}, |
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year={2025}, |
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doi={10.1038/s41598-025-94772-x} |
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} |
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