Feature Extraction
Transformers
English
remote-sensing
earth-observation
self-supervised-learning
multispectral
sar
rgb
depth
decur
resnet
vit
segformer
Instructions to use BiliSakura/DECUR-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BiliSakura/DECUR-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BiliSakura/DECUR-transformers")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BiliSakura/DECUR-transformers", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- decur-mit-b2-hha
- decur-mit-b2-rgb
- decur-mit-b5-hha
- decur-mit-b5-rgb
- decur-resnet50-dem
- decur-resnet50-rda-dem
- decur-resnet50-rda-rgb
- decur-resnet50-rda-s1
- decur-resnet50-rda-s2c
- decur-resnet50-rgb
- decur-resnet50-s1
- decur-resnet50-s2c
- decur-vit-small-patch16-dem
- decur-vit-small-patch16-rgb
- decur-vit-small-patch16-s1
- decur-vit-small-patch16-s2c
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- 5.55 kB
- 2.11 kB