sen12ms evaluation deep dive and documentation

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Sen12ms datasets evaluation (supervised models)

• document (single label). Make it more clear
• Document multi-label how the author did
• Document Sen12ms repo, 2 tasks (classification, semantic segmentation)
• Goal is to clearly explain to Colorado
• Simple vs full (Going over the code for dataset.py, simplified vs full (10 vs 17 class).
• Single vs multi
• S1, s2, rgb, vs s1/s2
• Different metrics
• K-mean usage (page 5)
• One class with no label (ice class)
• what does "probability" in this https://github.com/Berkeley-Data/SEN12MS/blob/taeil/splits/IGBP_probability_labels.pkl

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• Figuring out the difference with Sample patch (scene).
• Figuring out the sample size

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• Semantic segmentation (not the classification) - model are trained in https://github.com/lukasliebel/dfc2020_baseline

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• Semantic segmentation (not the classification) - model are trained in https://github.com/lukasliebel/dfc2020_baseline

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