Image Segmentation
PyTorch
Transformers
pytorch lightning
self-supervised learning
masked autoencoders
remote sensing
earth observation
multimodal
multitemporal
Instructions to use IGNF/MAESTRO_S2-NAIP-urban_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IGNF/MAESTRO_S2-NAIP-urban_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="IGNF/MAESTRO_S2-NAIP-urban_base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IGNF/MAESTRO_S2-NAIP-urban_base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md (#1)
Browse files- Update README.md (25b5a583f4023c44cb539d3f807333f5103644c5)
Co-authored-by: Antoine Labatie <alabatie@users.noreply.huggingface.co>
README.md
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license:
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pipeline_tag: image-segmentation
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tags:
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- semantic segmentation
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- pytorch
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library_name: pytorch
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We introduce **MAESTRO**, a tailored adaptation of the Masked Autoencoder (MAE) framework that effectively orchestrates the use of multimodal, multitemporal, and multispectral Earth Observation (EO) data. Evaluated on four EO datasets, MAESTRO sets a new state-of-the-art on tasks that strongly rely on multitemporal dynamics, while remaining highly competitive on tasks dominated by a single monotemporal modality.
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license: apache-2.0
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pipeline_tag: image-segmentation
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tags:
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- pytorch
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- self-supervised
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- transformers
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- multimodal
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- remote sensing
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library_name: pytorch
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datasets:
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- allenai/s2-naip
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We introduce **MAESTRO**, a tailored adaptation of the Masked Autoencoder (MAE) framework that effectively orchestrates the use of multimodal, multitemporal, and multispectral Earth Observation (EO) data. Evaluated on four EO datasets, MAESTRO sets a new state-of-the-art on tasks that strongly rely on multitemporal dynamics, while remaining highly competitive on tasks dominated by a single monotemporal modality.
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