Instructions to use smc/PANDA_ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smc/PANDA_ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/PANDA_ViT") 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_ViT") model = AutoModelForImageClassification.from_pretrained("smc/PANDA_ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sebastián Medina commited on
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An attempt to use a ViT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
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Example Image (1152,1152,3) 36 WSI patches: <img src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00bbc1482301d16de3ff63238cfd0b34_2.jpeg">
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An attempt to use a ViT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
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Example Image (1152,1152,3) 36 WSI patches: <img width="500" height="500" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00bbc1482301d16de3ff63238cfd0b34_2.jpeg">
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