Image Classification
timm
PyTorch
Safetensors
vision-transformer
swin
gravitational-lensing
strong-lensing
astronomy
astrophysics
Eval Results (legacy)
Instructions to use parlange/swin-gravit-a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use parlange/swin-gravit-a1 with timm:
import timm model = timm.create_model("hf_hub:parlange/swin-gravit-a1", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Upload Swin model from experiment a1
Browse files
README.md
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- astronomy
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- astrophysics
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datasets:
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metrics:
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- accuracy
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- auc
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- f1
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model-index:
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- name: Swin-a1
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results:
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- astronomy
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- astrophysics
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datasets:
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- parlange/gravit-c21
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metrics:
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- accuracy
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- auc
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- f1
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paper:
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- title: "GraViT: A Gravitational Lens Discovery Toolkit with Vision Transformers"
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url: "https://arxiv.org/abs/2509.00226"
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authors: "Parlange et al."
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model-index:
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- name: Swin-a1
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results:
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