Instructions to use Jakir057/banknote18k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jakir057/banknote18k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Jakir057/banknote18k") 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("Jakir057/banknote18k") model = AutoModelForImageClassification.from_pretrained("Jakir057/banknote18k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jakir057/banknote18k: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/Jakir057/banknote18k/resolve/main/training_args.bin
- Command line
-
hf download hf://Jakir057/banknote18k/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jakir057/banknote18k/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 0ea0beed0313ffc9366b74ba8fe55e59641f6211f17842b753564041929d451b
- Size of remote file:
- 4.03 kB
- SHA256:
- f0333b9e9e0b9e860469b17645e692d91a8171e54dd0b1d4565e98bff953d4d7
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