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 pytorch_model.bin from Jakir057/banknote18k: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Jakir057/banknote18k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jakir057/banknote18k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jakir057/banknote18k/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 8b9625c94b30cc6f8f313e2a114b721758c5826c6925b39b600be84517ba2e59
- Size of remote file:
- 343 MB
- SHA256:
- 98d51b267c59249ff7061cba5f38fb16454bffdec99a2bf796aded8a392e3f61
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