Instructions to use rendy-k/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendy-k/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rendy-k/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("rendy-k/image_classification") model = AutoModelForImageClassification.from_pretrained("rendy-k/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rendy-k/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/rendy-k/image_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://rendy-k/image_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rendy-k/image_classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 00348d6feadb4dae0e1fc3a53598a604c4e49c0e8dcc8524f4c5a0547a837270
- Size of remote file:
- 4.6 kB
- SHA256:
- 61e28b8536b610747a7077cdb88755542ce648c6d5b585a3d4e06b801375eb95
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