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:
- bc8c99262febbb3e9d646a4a3dfdbb196c606aef34d4e354f77904bc14419d0b
- Size of remote file:
- 343 MB
- SHA256:
- f520f2b8b35e41848231404eeff38debe218d78a6a6f4e31692a90b2310af9d6
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