Instructions to use flatmoon102/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flatmoon102/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="flatmoon102/image_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("flatmoon102/image_classification") model = AutoModelForImageClassification.from_pretrained("flatmoon102/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from flatmoon102/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/flatmoon102/image_classification/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://flatmoon102/image_classification@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/flatmoon102/image_classification/resolve/refs%2Fpr%2F1/training_args.bin
4.09 kB
- Xet hash:
- cad00660fa5d5decf5da22173576ad230d71d90f8e652b2901b72e91513e22bc
- Size of remote file:
- 4.09 kB
- SHA256:
- 306b87d9ba5f723e9d1b6f882d7d0d77790f6cc99b81c3aa3a29e36bbd8fc0ce
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