Image Segmentation
PyTorch
Transformers
pytorch lightning
self-supervised learning
masked autoencoders
remote sensing
earth observation
multimodal
multitemporal
Instructions to use IGNF/MAESTRO_FLAIR-HUB_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IGNF/MAESTRO_FLAIR-HUB_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="IGNF/MAESTRO_FLAIR-HUB_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IGNF/MAESTRO_FLAIR-HUB_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download dataset_splits/TreeSatAI-TS/test.csv from IGNF/MAESTRO_FLAIR-HUB_base: direct link, hf CLI and curl.
- Browser
- Download file 759 kB
-
https://huggingface.co/IGNF/MAESTRO_FLAIR-HUB_base/resolve/main/dataset_splits/TreeSatAI-TS/test.csv
- Command line
-
hf download hf://IGNF/MAESTRO_FLAIR-HUB_base/dataset_splits/TreeSatAI-TS/test.csv
-
curl -L -o test.csv https://huggingface.co/IGNF/MAESTRO_FLAIR-HUB_base/resolve/main/dataset_splits/TreeSatAI-TS/test.csv
759 kB
File too large to display, you can check the raw version instead.