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 (blstm.hdf5) Encoder-decoder (enc_dec.hdf5) Long short-term memory (lstm.hdf5) Baseline Majority vote classifier (baseline.model) Semi-supervised models Cluster-then-predict models: Bayesian Gaussian mixture model (gmm.model) Bayesian mixture model (bmm.model) K-means clustering (kmeans.model) Label propagation (label_spreading.model) Self-trained classifier (self_trainer.model) Supervised models Balanced random forest (rf_bal.model) Easy ensemble (easy_ensemble.model) K-nearest neighbors (knn.model) Random forest (rf.model) Support vector machines (svm.model) Loading Instructions: 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).