[ ] Add analysis functions for plots (see ITI paper)
[ ]
ML-Ready Data
Demo showcasing how we can create ML-Ready Data using scripts and parallelization. We will create 3 dataset types for each tutorial 1) single channel datasets, 2) multi-channel datasets, and 3) multi-channel time-series datasets.
[ ] Numpy Dataset
[ ] Image Dataset
[ ] xarray.Dataset
Machine Learning DataModules
Demo Training Datasets + DataLoaders
These demos will showcase how we can create datasets and dataloaders for different datastructures. We will focus on PyTorch datasets for three data structures: 1) numpy.ndarray, 2) .png/.jpeg/… image files, and 3) xarray.Datasets. Well will discuss things like global dataset normalization and (random) patching.
[ ] Demo DataLoader with numpy (see example notebook)
[ ] Demo DataLoader with RasterVision (see example)
These demos will showcase how we can create datasets and dataloaders for inference (making predictions for new datasets). . We will focus on PyTorch datasets for three data structures: 1) numpy.ndarray, 2) .png/.jpeg/… image files, and 3) xarray.Datasets. We will discuss additional transformations that are needed for the datasets like training dataset normalizing and sliding window patching.
Preprocessing
SDO Pipeline
ML-Ready Data
Machine Learning DataModules
Demo Training Datasets + DataLoaders
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