Instructions to use nathanReitinger/FASHION-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nathanReitinger/FASHION-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nathanReitinger/FASHION-vision") 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("nathanReitinger/FASHION-vision") model = AutoModelForImageClassification.from_pretrained("nathanReitinger/FASHION-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nathanReitinger/FASHION-vision: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/nathanReitinger/FASHION-vision/resolve/main/training_args.bin
- Command line
-
hf download hf://nathanReitinger/FASHION-vision/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nathanReitinger/FASHION-vision/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 078e8b45c4857a062ed3a369d5c2de710833ec6a22299634d7a28e047285ae45
- Size of remote file:
- 5.05 kB
- SHA256:
- f84fb4de6bdf7d0d30b8f9734228ab747e633a394c4a9b8753b5ba7ff5d1ef2b
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