IGNF
/

MAESTRO_S2-NAIP-urban_base

Image Segmentation

pytorch lightning

self-supervised learning

masked autoencoders

earth observation

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@@ -8,6 +8,7 @@ tags:
 - masked autoencoders
 - transformers
 - remote sensing
 - multimodal
 - multitemporal
 library_name: pytorch
@@ -103,6 +104,7 @@ Below are cross-dataset evaluation results obtained with these guidelines on Tre
 ## 🚀 Getting started
 Prerequisites:
 - Fetch [Dataset splits](https://huggingface.co/IGNF/MAESTRO_S2-NAIP-urban_base/tree/main/dataset_splits) and move them to each dataset directory
 - Fetch [model weights](MAESTRO_S2-NAIP-urban_base/checkpoints/pretrain-epoch=14.ckpt) and move them into `/path/to/experiments/MAESTRO_S2-NAIP-urban_base/checkpoints/`
 - Fetch [model configuration](MAESTRO_S2-NAIP-urban_base/.hydra/config_resolved.yaml) and move it into `/path/to/experiments/MAESTRO_S2-NAIP-urban_base/.hydra/`

 - masked autoencoders
 - transformers
 - remote sensing
+- earth observation
 - multimodal
 - multitemporal
 library_name: pytorch
 ## 🚀 Getting started
 Prerequisites:
+- Clone MAESTRO's [code repository](https://github.com/ignf/maestro)
 - Fetch [Dataset splits](https://huggingface.co/IGNF/MAESTRO_S2-NAIP-urban_base/tree/main/dataset_splits) and move them to each dataset directory
 - Fetch [model weights](MAESTRO_S2-NAIP-urban_base/checkpoints/pretrain-epoch=14.ckpt) and move them into `/path/to/experiments/MAESTRO_S2-NAIP-urban_base/checkpoints/`
 - Fetch [model configuration](MAESTRO_S2-NAIP-urban_base/.hydra/config_resolved.yaml) and move it into `/path/to/experiments/MAESTRO_S2-NAIP-urban_base/.hydra/`