Hessisches Ministerium für Wissenschaft und Kunst
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Research Data Open Access Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach(2022-05-10) Augustat, Nick; Müller, Erik Malte; Endres, Dominik; Chuang, Li-Ching; Panitz, Christian; Stolz, ChristopherResearch Data Open Access Activity classification models(Jannis Gottwald) Gottwald, JannisResearch Data Restricted Nature 4.0: A networked sensor system for integrated biodiversity monitoringBald, Lisa; Zeuss, Dirk; Frieß, Nicolas; Wöllauer, Stephan; Kohlbrecher, Viviane; Lindner, Kim; Farwig, NinaResearch Data Open Access Test data for tRackIT R-Package(Jannis Gottwald) Gottwald, JannisResearch Data Open Access Radiotracking campaign 2019 - raw dataFrieß, Nicolas; Farwig, Nina; Nauss, Thomas; Reudenbach, Christoph; Quillfeldt, Petra; Gottwald, Jannis; Rösner, Sascha; Lindner, Kim; Masello, Juan F.; Ludwig, MarvinResearch Data Open Access Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model- Springer2024(Philipps-Universität marburg, 07.05.2021) Endres, Dominik; Meibodi, Neda; Meibodi, NedaThis repository contains the files and data necessary to recreate the results from the paper Meibodi, N., Abbasi, H., Schubö, A. et al. Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model. Comput Brain Behav 7, 268–285 (2024). https://doi.org/10.1007/s42113-024-00197-6 Please go to Version 2 if you are interested to see the files related to the other pubplication N.Meibodi, H.Abbasi, A. Schuboe, D. Endres (2021) A Model of Selection History in Visual Attention, Proceedings of the 2021 Conference of the Society of Cognitive Science, Vienna, Austria.Research Data Open Access Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep LearningVogelbacher, Markus; Strehmann, Finja; Bellafkir, Hicham; Mühling, Markus; Korfhage, Nikolaus; Schneider, Daniel; Rösner, Sascha; Schabo, Dana G.; Farwig, Nina; Freisleben, BerndResearch Data Open Access Processing unexpected social feedback in depressionKirchner, LukasResearch Data Open Access Integration of optic flow into the sky compass network in the brain of the desert locust (Frontiers Version)Zittrell, Frederick; Pabst, Kathrin; Carlomagno, Elena; Rosner, Ronny; Pegel, Uta; Endres, Dominik; Homberg, UweResearch Data Open Access Recognition of European mammals and birds in camera trap images using deep neural networks(Philipps-Universität Marburg) Schneider, Daniel; Lindner, Kim; Vogelbacher, Markus; Bellafkir, Hicham; Mühling, Markus; Farwig, Nina; Freisleben, BerndThis record contains the trained models and the test data sets presented in the papers "Recognizing European mammals and birds in camera trap images using convolutional neural networks" (Schneider et al, 2023) and "Recognition of European mammals and birds in camera trap images using deep neural networks" (Schneider et al., 2024). In these publications, we present deep neural network models to recognize both mammal and bird species in camera trap images. In the archive files "model2023_ConvNextBase.tar" and "model2023_EfficientNetV2.tar" as well as "model2024_ConvNextBase_species.tar" and "model2024_ConvNextBase_taxonomy.tar" we provide downloads of the best trained models from our 2023 and 2024 papers, respectively. All models are provided in the Tensorflow2 SavedModel format (https://www.tensorflow.org/guide/saved_model). A script to load and run the models can be found in our Git-Repository: https://github.com/umr-ds/Marburg-Camera-Traps. There we also provide a code snippet to perform predictions with these models. In the archive files "data_MOF.tar" and "data_BNP.tar", we provide downloads for our Marburg Open Forest (MOF) and Białowieża National Park (BNP) data sets, consisting of about 2,500 and 15,000 labeled camera trap images, respectively. The files contain two folders named "img" and "md", respectively. The "img" folder contains the images grouped in subfolders by recording date and camera trap id. The "md" folder contains the metadata for each image, which constists of the bounding box detections obtained using the MegaDetector model (https://github.com/agentmorris/MegaDetector). The metadata is grouped into yaml-files for each label at different taxonomic levels.
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