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
Remove config.yaml - superseded by config.json
Browse files- config.yaml +0 -6
config.yaml
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model_class: SegModels.SwinV2.Swinv2Segmenter
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model_pretrained: microsoft/swinv2-large-patch4-window12-192-22k
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resolution: natural
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pad: 1472
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multilabel: true
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num_classes: 4
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