Instructions to use itsmefaiz/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itsmefaiz/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="itsmefaiz/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("itsmefaiz/image_classification") model = AutoModelForImageClassification.from_pretrained("itsmefaiz/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from itsmefaiz/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/itsmefaiz/image_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://itsmefaiz/image_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/itsmefaiz/image_classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 64f7fefc494ae21844e941a45977513d7dc7ccb35f422610b60204139cb65b7e
- Size of remote file:
- 4.6 kB
- SHA256:
- 1644aa05fdf1c6ca349b0f203f92a6e69ef2e2c319fbcab9b7e264b769747b2c
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