Pre-trained Models for SMP Classification and Segmentation

This dataset provides access to pre-trained models that were used for SnowMicroPen profile classification and segmentation. The models were trained on a part of the MOSAiC SMP dataset, available on https://doi.pangaea.de/10.1594/PANGAEA.935554. The labeled training data consists mostly of profiles from leg three of the expedition (January - May 2020), some profiles from leg one and two, and no profiles from leg four. Please refer to the snowdragon GitHub repository (https://github.com/liellnima/snowdragon) to access the models' training code and be directed to current publications. The following trained models are available here (alphabetically ordered): Artificial neural networks Bi-directional long short-term memory <em>(blstm.hdf5)</em> Encoder-decoder <em>(enc_dec.hdf5)</em> Long short-term memory <em>(lstm.hdf5)</em> Baseline Majority vote classifier <em>(baseline.model)</em> Semi-supervised models Cluster-then-predict models: Bayesian Gaussian mixture model <em>(gmm.model)</em> Bayesian mixture model <em>(bmm.model)</em> K-means clustering <em>(kmeans.model)</em> Label propagation <em>(label_spreading.model)</em> Self-trained classifier <em>(self_trainer.model)</em> Supervised models Balanced random forest <em>(rf_bal.model)</em> Easy ensemble <em>(easy_ensemble.model)</em> K-nearest neighbors <em>(knn.model)</em> Random forest <em>(rf.model)</em> Support vector machines <em>(svm.model)</em> <br> <em>Loading Instructions:</em><br> The models with the file-ending ".model" are pickeled Python objects and can be loaded with ``pickle.load(your_model.model)``. The random forest must be loaded with ``joblib.load(rf.model)``. All artificial neural networks are h5py.File objects (tf.keras models) and can be loaded with ``tf.keras.models.load_model(your_ann.model)``.