Transformers
ONNX
Safetensors
swinv2_segmenter
retinal-fundus
artery-vein-segmentation
medical-imaging
ophthalmology
swinv2
custom_code
Instructions to use JonathanFhima/RLAD-SwinV2-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JonathanFhima/RLAD-SwinV2-Large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JonathanFhima/RLAD-SwinV2-Large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add Tables 3-4, stop asserting UZLF's release status without evidence
Browse files
README.md
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| Source | Status |
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| UZLF | KU Leuven
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| GRAPE | CC0 1.0 |
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| MAGRABIA | CC BY-NC 4.0 |
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| MESSIDOR | redistribution and commercial use explicitly prohibited |
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## How it works
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A SwinV2-Large backbone (`microsoft/swinv2-large-patch4-window12-192-22k`
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| Source | Status |
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| UZLF | KU Leuven-associated fundus dataset; we haven't found a public release page or license terms for it |
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| GRAPE | CC0 1.0 |
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| MAGRABIA | CC BY-NC 4.0 |
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| MESSIDOR | redistribution and commercial use explicitly prohibited |
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## Ablations
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## How it works
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A SwinV2-Large backbone (`microsoft/swinv2-large-patch4-window12-192-22k`
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