Instructions to use octava/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use octava/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="octava/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("octava/image_classification") model = AutoModelForImageClassification.from_pretrained("octava/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from octava/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/octava/image_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://octava/image_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/octava/image_classification/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- bdec4f6bb23154245747449c858e7babccaffc5bc6b612d296e7a4751cd4b090
- Size of remote file:
- 4.47 kB
- SHA256:
- 7f7e9e943ff7d480e1794e1963e4204ac0a34ec8ae404c39a1e55fc595c01783
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