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")# 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
- Xet hash:
- 00693a9a841234757881b5a6390ba1c9b2aa9848cee9a45e9afb810793414157
- Size of remote file:
- 343 MB
- SHA256:
- f4cc3200f8888314b9d99c2ed1b2351d755137154ebec6561032b99438f706aa
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