Instructions to use crangana/trained-race with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crangana/trained-race with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="crangana/trained-race") 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("crangana/trained-race") model = AutoModelForImageClassification.from_pretrained("crangana/trained-race") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f525f8d53443baa6c23f36004f2b440a6ca0bca27f8e7b00a5b760cc5916d666
- Size of remote file:
- 4.03 kB
- SHA256:
- 5555c3e5b69f073ca28130824b8f601ad42d76af2207261b20c36b8cb572d897
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