Kenya/Nandi crop type inference #167
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An initial idea is to split the whole county into 4K windows (each is 128*128 pixel), and perform inference on each window. Here, the approach is to create patches per window, and then run model forward on all the patches.
Command for local run:
python -m rslp.rslearn_main model predict --config data/helios/v2_nandi_crop_type/finetune_s2.yaml