Instructions to use rdtm/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdtm/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rdtm/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("rdtm/image_classification") model = AutoModelForImageClassification.from_pretrained("rdtm/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51e24ddb510dfa83d62083351603644a5b33f8a611d21c56733fd5fbfe83c216
- Size of remote file:
- 343 MB
- SHA256:
- 5767472f6449df09ee74d2a71b8fd1c5dc0e9d21662672b277d9629ed56b1967
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