Feature Extraction
Transformers
Safetensors
English
closp
remote-sensing
text-to-image-retrieval
multimodal
geospatial
SAR
multispectral
crisis-management
earth-observation
contrastive-learning
custom_code
Instructions to use DarthReca/CLOSP-VS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarthReca/CLOSP-VS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DarthReca/CLOSP-VS", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DarthReca/CLOSP-VS", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9744b5824c22dd29792345121971dea2ed2017c4bbca1a62cdd3c8511789089e
- Size of remote file:
- 271 MB
- SHA256:
- fde74df15d7ac909ae2fd6e95ca84f8281e38da76bb287e0a55c94bb4d87deea
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