Instructions to use recursionpharma/OpenPhenom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use recursionpharma/OpenPhenom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="recursionpharma/OpenPhenom", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("recursionpharma/OpenPhenom", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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# Model Card for
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Channel-agnostic image encoding model designed for microscopy image featurization.
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The model uses a vision transformer backbone with channelwise cross-attention over patch tokens to create contextualized representations separately for each channel.
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# Model Card for OpenPhenom-S/16
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Channel-agnostic image encoding model CA-MAE with a ViT-S/16 encoder backbone designed for microscopy image featurization.
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The model uses a vision transformer backbone with channelwise cross-attention over patch tokens to create contextualized representations separately for each channel.
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