Instructions to use pawlo2013/kimchi-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pawlo2013/kimchi-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pawlo2013/kimchi-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("pawlo2013/kimchi-classification") model = AutoModelForImageClassification.from_pretrained("pawlo2013/kimchi-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pawlo2013/kimchi-classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/pawlo2013/kimchi-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pawlo2013/kimchi-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pawlo2013/kimchi-classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 3f231a4cf5c707d0867ae9d43369ec372b38ab6bef00c26fd2f153ea6ed9b890
- Size of remote file:
- 343 MB
- SHA256:
- 98b39ea8f28adc70b5fa2b5be32d3d2ecf9a078b21d0ac6599e9257b53161b83
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