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Microplastics_SEM
AI summary Read the abstract
This is a research dataset and code repository — not a standalone paper — associated with a study that trained deep learning models to automatically detect and classify microplastics in scanning electron microscope images, supporting more efficient and reproducible analysis.
Here are the datasets for the paper (Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning) published the Science of the Total Environment. The complete training dataset of semantic segmentation and shape classification is in dataset1.zip. The dataset of instance segmentation is present in multiple forms in dataset2.zip. The experiment setting for each micrograph was recorded together with the original SEM micrographs and all ground-truth labels in the file dataset3.zip. Main results were saved in result.zip. Paper link: https://doi.org/10.1016/j.scitotenv.2022.153903 Code link: https://github.com/benjamin3344/Microplastics_Unet Updates on 2026-02-22: Pre-trained weights for models of different sizes (4*5 fold) are included in result_weights.zip. Any further cooperation can be addressed to Professor Jane Howe.
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Microplastics_SEM
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This is a research dataset and code repository — not a standalone paper — associated with a study that trained deep learning models to automatically detect and classify microplastics in scanning electron microscope images, supporting more efficient and reproducible analysis.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.