Instructions to use Dhika/raildefect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect") 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("Dhika/raildefect") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f09b0dfe2678a590d2df7cd3fb40840989f59baeec0e3f94c9af0cfe1841ae57
- Size of remote file:
- 3.9 kB
- SHA256:
- 8e30179e9b5fc0e85dd075335a3d5b1398022bd061c22dac364188edeb4b5283
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