Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models
Paper • 2608.00976 • Published
How to use myeongkyunkang/siglip2-so400m-patch16-512-lofi-medg with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-feature-extraction", model="myeongkyunkang/siglip2-so400m-patch16-512-lofi-medg") # Load model directly
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
processor = AutoProcessor.from_pretrained("myeongkyunkang/siglip2-so400m-patch16-512-lofi-medg")
model = AutoModelForZeroShotImageClassification.from_pretrained("myeongkyunkang/siglip2-so400m-patch16-512-lofi-medg", device_map="auto")Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models
This repository contains the model described in Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models.
For more details, please refer to the code.
Base model
google/siglip2-so400m-patch16-512