Feature Extraction
Transformers
Safetensors
English
remote-sensing
earth-observation
self-supervised-learning
sentinel-2
sentinel-1
multispectral
sar
vision
ssl4eo
mae
moco
dino
data2vec
vit
resnet
Instructions to use BiliSakura/SSL4EO-S12-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BiliSakura/SSL4EO-S12-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BiliSakura/SSL4EO-S12-transformers")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BiliSakura/SSL4EO-S12-transformers", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- ssl4eo-resnet18-rgb-moco
- ssl4eo-resnet18-s2c-moco
- ssl4eo-resnet50-s1-moco
- ssl4eo-resnet50-s2c-dino
- ssl4eo-resnet50-s2c-moco
- ssl4eo-vit-base-patch16-s1-mae
- ssl4eo-vit-base-patch16-s2c-mae
- ssl4eo-vit-huge-patch14-s1-mae
- ssl4eo-vit-huge-patch14-s2c-mae
- ssl4eo-vit-large-patch16-s1-mae
- ssl4eo-vit-large-patch16-s2c-mae
- ssl4eo-vit-small-patch16-s1-mae
- ssl4eo-vit-small-patch16-s2c-data2vec
- ssl4eo-vit-small-patch16-s2c-dino
- ssl4eo-vit-small-patch16-s2c-mae
- ssl4eo-vit-small-patch16-s2c-moco
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- 7.27 kB