Instructions to use tigeryi/imagenet1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tigeryi/imagenet1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tigeryi/imagenet1k") 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("tigeryi/imagenet1k") model = AutoModelForImageClassification.from_pretrained("tigeryi/imagenet1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tigeryi/imagenet1k: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/tigeryi/imagenet1k/resolve/main/training_args.bin
- Command line
-
hf download hf://tigeryi/imagenet1k/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tigeryi/imagenet1k/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 1a44f047253b1099189f18e0a58a6fa1759b86d6cb2ce4122cdc7d9d77948fa3
- Size of remote file:
- 4.98 kB
- SHA256:
- 11d852ed0ea50bad28da851992b7a413c9a2786d5851d87045407cf0e02ea853
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