Instructions to use Bazaar/cv_chinese_medicine_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_chinese_medicine_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_chinese_medicine_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("Bazaar/cv_chinese_medicine_classification") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_chinese_medicine_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/dangshen.jpg from Bazaar/cv_chinese_medicine_classification: direct link, hf CLI and curl.
- Browser
- Download file 118 kB
-
https://huggingface.co/Bazaar/cv_chinese_medicine_classification/resolve/main/images/dangshen.jpg
- Command line
-
hf download hf://Bazaar/cv_chinese_medicine_classification/images/dangshen.jpg
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curl -L -o dangshen.jpg https://huggingface.co/Bazaar/cv_chinese_medicine_classification/resolve/main/images/dangshen.jpg
118 kB

- Xet hash:
- 695456e807064e864dd4266c6764e21c79025f17cd10815d68e92d013279e17e
- Size of remote file:
- 118 kB
- SHA256:
- c83e26c79f905d61d83474a46bc7ae85fee088da83d6c5449dfcaad3857db352
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