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:
- 5fb9f53eb3c28bfe9ed97ec546a2e9c22a3a9d1571d3253b53ecbf1c4089e5b0
- Size of remote file:
- 94.4 MB
- SHA256:
- b576d52e2a43192a2861de9ad21217d93f067a09cb784988fbc886f2ef3a432e
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