Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use hossay/stool-condition-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hossay/stool-condition-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hossay/stool-condition-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hossay/stool-condition-classification") model = AutoModelForImageClassification.from_pretrained("hossay/stool-condition-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- f1
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model-index:
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- name: stool-condition-classification
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results:
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metrics:
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- accuracy
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- f1
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- src: https://i.natgeofe.com/n/548467d8-c5f1-4551-9f58-6817a8d2c45e/NationalGeographic_2572187_16x9.jpg
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model-index:
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- name: stool-condition-classification
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results:
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