L o a d i n g
Organization
Swiss Federal Institute of Aquatic Science and Technology (Eawag) - view all
Update frequencyunknown
Last updated3 weeks ago
Overviewdeep convolutional neural networkzooplankton images
This dataset contains deep features extracted from Aquascope images taken in lake Greifensee from 2018 to 2025. Deep features were extracted using a MobileNetV3-Large convolutional neural network pre-trained on ImageNet with no fine tuning. As such they are generic but similar features have been proven to still be relevant for plankton images (Orenstein and Beijbom, 2017). They can serve as a basis for further classification or trait-based ecological studies. More information about Aquascope: https://doi.org/10.1016/j.watres.2021.117524 https://doi.org/10.25678/0004BW
Additional Information
KeyValue
Harvest Object Idb34f3f3b-e62b-4aad-bbd9-10b8903d6cff
Harvest Source Idd0230d8d-fb2c-4caf-94e8-8ad52bd38ad9
Harvest Source TitleThe Eawag Research Data Institutional Repository