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 config.json from JonathanFhima/RLAD-SwinV2-Large: direct link, hf CLI and curl.
- Browser
- Download file 426 Bytes
-
https://huggingface.co/JonathanFhima/RLAD-SwinV2-Large/resolve/main/config.json
- Command line
-
hf download hf://JonathanFhima/RLAD-SwinV2-Large/config.json
-
curl -L -o config.json https://huggingface.co/JonathanFhima/RLAD-SwinV2-Large/resolve/main/config.json
426 Bytes
| { | |
| "model_type": "swinv2_segmenter", | |
| "model_pretrained": "microsoft/swinv2-large-patch4-window12-192-22k", | |
| "vit_patches_size": 4, | |
| "resolution": "natural", | |
| "num_classes": 4, | |
| "multilabel": true, | |
| "image_size": 1444, | |
| "auto_map": { | |
| "AutoConfig": "LUNetv2.Swinv2SegmenterConfig", | |
| "AutoModel": "LUNetv2.Swinv2Segmenter", | |
| "AutoImageProcessor": "image_processing_lunetv2.Swinv2SegmenterImageProcessor" | |
| } | |
| } | |