Image-to-Image
Transformers
PyTorch
swinv2
physics
pde
neural-operator
cfd
fluid-dynamics
poseidon
scOT
Instructions to use empirischtech/poseidon-b-joint-cyl-kh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use empirischtech/poseidon-b-joint-cyl-kh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="empirischtech/poseidon-b-joint-cyl-kh")# Load model directly from transformers import AutoImageProcessor, ScOT processor = AutoImageProcessor.from_pretrained("empirischtech/poseidon-b-joint-cyl-kh") model = ScOT.from_pretrained("empirischtech/poseidon-b-joint-cyl-kh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
added dataset reference
Browse files
README.md
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library_name: transformers
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pipeline_tag: image-to-image
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base_model: camlab-ethz/Poseidon-B
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---
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# Poseidon-B — Joint fine-tune (Cylinder + Kelvin–Helmholtz)
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library_name: transformers
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pipeline_tag: image-to-image
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base_model: camlab-ethz/Poseidon-B
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datasets:
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- empirischtech/pyfr-cylinder-kh
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---
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# Poseidon-B — Joint fine-tune (Cylinder + Kelvin–Helmholtz)
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