DaphnAI: A Deep Learning Approach for High-Throughput Zooplankton Community Analysis

Contents dataset.zip A compressed folder that contains: images/: Raw image data used for training and validation and testing. labels/: YOLO-format .txt annotation files for the corresponding images. out/: Images annotated with segmentation polygons and class labels, useful for visualization and quality control. trainer.sh A shell script to initiate YOLOv12 model training. It configures the dataset, model architecture, and training hyperparameters. data_species.yaml YAML file specifying dataset structure and class labels. It defines 7 classes: ["D_pulex", "ballooned", "copepod", "egg", "resting_egg", "D_galeata", "S_vetulus"]. chunker.py A utility script for preprocessing high-resolution images. It splits large images into chunks for more effective training on high-resolution biological data. Supports .tif, .png, and .jpg formats. d_pulex_missclass_analysis.ipynb A Jupyter notebook used for post-training analysis, focusing on the misclassification of Daphnia pulex. autosplit_train_res.csvA csv file containing the inference results of the test images (used for the analysis of misclassification of Daphnia pulex) DaphnAI.pt The weights of the model (pre trained-model). Interactive Jupyter tutorial (tutorial.ipynb) A step-by-step notebook for inference, visualization, and data extraction with code and explanations.