How to use smc/PANDA_ConvNeXT_K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/PANDA_ConvNeXT_K") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("smc/PANDA_ConvNeXT_K") model = AutoModelForImageClassification.from_pretrained("smc/PANDA_ConvNeXT_K")
An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
ISUP 0:
ISUP 1: ISUP 2: ISUP 3: ISUP 4: ISUP 5:
ISUP 2:
ISUP 3:
ISUP 4:
ISUP 5:
