Instructions to use aditira/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aditira/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aditira/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("aditira/image_classification") model = AutoModelForImageClassification.from_pretrained("aditira/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aditira/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 344 MB
-
https://huggingface.co/aditira/image_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://aditira/image_classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aditira/image_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
344 MB
- Xet hash:
- e357b226fc4646974160326a42298da85223f486533ab2580d0bd9c26adabf29
- Size of remote file:
- 344 MB
- SHA256:
- 68fcce86d613717befeff5e4bced2a61b0eb65d4e7bac91d7af7f5644dfae603
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