Instructions to use rchan26/dit_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rchan26/dit_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rchan26/dit_base") 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("rchan26/dit_base") model = AutoModelForImageClassification.from_pretrained("rchan26/dit_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc78a7a4f21073178e54ec3fb3df4a9a80e325497c1b51629f3d54b532842773
- Size of remote file:
- 3.38 kB
- SHA256:
- d210168ea04f5658c89800d07881a6797e1cb1af649723be57b31bfa96a97d66
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