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
File size: 166 Bytes
59f2657 | 1 2 3 4 5 6 7 | {
"image_processor_type": "Swinv2SegmenterImageProcessor",
"auto_map": {
"AutoImageProcessor": "image_processing_lunetv2.Swinv2SegmenterImageProcessor"
}
}
|