Image Classification
Transformers
Safetensors
total_classifier
feature-extraction
radiology
ct
organ
classification
custom_code
Instructions to use ianpan/total-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/total-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ianpan/total-classifier", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ianpan/total-classifier", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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available labels from TotalSegmentator. The classification labels were generated from the provided segmentation labels.
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Note that the model expects one channel. If you create a multi-channel image using multiple CT windows, simply take the mean across channels.
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## Example Usage
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available labels from TotalSegmentator. The classification labels were generated from the provided segmentation labels.
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Note that the model expects one channel. If you create a multi-channel image using multiple CT windows, simply take the mean across channels.
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The model also expects 8-bit input (converted to float). Thus if your CT volume is in Hounsfield units, you can apply a standard window,
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such as soft tissue (level=50, width=400), before inputting it into the model.
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## Example Usage
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