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 training_args.bin from pawlo2013/kimchi-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/pawlo2013/kimchi-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://pawlo2013/kimchi-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pawlo2013/kimchi-classification/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- 164cc4bf6eaeb7a6b57f857d55aaf64c9aa7633dadc17b15cc138008eab468bf
- Size of remote file:
- 4.47 kB
- SHA256:
- 1b306432bd23b5e56bd50cbe3c502f9402db4cdd4b591cbc91dbe224e99d58ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.