Instructions to use smc/PANDA_ConvNeXT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smc/PANDA_ConvNeXT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/PANDA_ConvNeXT") 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") model = AutoModelForImageClassification.from_pretrained("smc/PANDA_ConvNeXT") - Notebooks
- Google Colab
- Kaggle
Sebastián Medina commited on
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Parent(s): ffae2f1
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README.md
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# PANDA_ConvNeXT
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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
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# PANDA_ConvNeXT
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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
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Example Images (1152,1152,3) 36 WSI patches:
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ISUP 0:
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<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c02d3bb3a62519b31c63d0301c6843e_0.jpeg">
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ISUP 1:
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<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0cee71ab57422e04f76e09ef2186fcd5_1.jpeg">
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ISUP 2:
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<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00bbc1482301d16de3ff63238cfd0b34_2.jpeg">
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ISUP 3:
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<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c5c2d16c0f2e399b7be641e7e7f66d9_3.jpeg">
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ISUP 4:
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<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c88d7c7033e2048b1068e208b105270_4.jpeg">
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ISUP 5:
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<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00c15b23b30a5ba061358d9641118904_5.jpeg">
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