Instructions to use comdet/liver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comdet/liver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="comdet/liver") 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("comdet/liver") model = AutoModelForImageClassification.from_pretrained("comdet/liver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from comdet/liver: direct link, hf CLI and curl.
- Browser
- Download file 752 Bytes
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https://huggingface.co/comdet/liver/resolve/main/README.md
- Command line
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hf download hf://comdet/liver/README.md
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curl -L -o README.md https://huggingface.co/comdet/liver/resolve/main/README.md
752 Bytes
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: liver | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9509394764900208 | |
| # liver | |
| Autogenerated by HuggingPics🤗🖼️ | |
| Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). | |
| Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). | |
| ## Example Images | |
| #### FFS | |
|  | |
| #### cystic | |
|  | |
| #### solid | |
|  |