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
Download example.png from JonathanFhima/RLAD-SwinV2-Large: direct link, hf CLI and curl.
- Browser
- Download file 1.43 MB
-
https://huggingface.co/JonathanFhima/RLAD-SwinV2-Large/resolve/main/example.png
- Command line
-
hf download hf://JonathanFhima/RLAD-SwinV2-Large/example.png
-
curl -L -o example.png https://huggingface.co/JonathanFhima/RLAD-SwinV2-Large/resolve/main/example.png
1.43 MB

- Xet hash:
- 0faebc044a80664a5733a27d3bc06be4cc9d2a9ed3b032227ccfc03399533c22
- Size of remote file:
- 1.43 MB
- SHA256:
- db315375d8957b9e5459527274b9aa60fac246e8bab93e7541f1030ce56ce5d4
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