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
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CLOSP (Contrastive Language Optical SAR Pretraining) is a multimodal architecture designed for text-to-image retrieval.
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It creates a unified embedding space for text, Sentinel-2 (MSI), and Sentinel-1 (SAR) data.
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The CLOSP-
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## Model Details
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The model uses three separate encoders: one for text, one for Sentinel-1 (SAR) data, and one for Sentinel-2 (MSI) data.
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CLOSP (Contrastive Language Optical SAR Pretraining) is a multimodal architecture designed for text-to-image retrieval.
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It creates a unified embedding space for text, Sentinel-2 (MSI), and Sentinel-1 (SAR) data.
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The CLOSP-VS variant uses a ViT-small vision backbone.
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## Model Details
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The model uses three separate encoders: one for text, one for Sentinel-1 (SAR) data, and one for Sentinel-2 (MSI) data.
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