Pre-trained Models for SMP Classification and Segmentation
- 1. Mila & McGill University
- 2. Swiss Federal Institute for Forest, Snow and Landscape Research
- 3. Numenta
Description
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)
- Cluster-then-predict models:
- 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)``.
Notes
Files
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Additional details
Related works
- Is supplemented by
- Conference paper: https://www.climatechange.ai/papers/neurips2021/48 (URL)
- Presentation: 10.5194/egusphere-egu21-15637 (DOI)
- References
- Dataset: https://doi.pangaea.de/10.1594/PANGAEA.935554 (URL)