Instructions to use Dhika/raildefect2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect2") 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/raildefect2") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb0665e400bb5eb4f6fe98d5d7a28dcafe54b8697786edf31d8f2a87cd5b9f6a
- Size of remote file:
- 343 MB
- SHA256:
- 5aea1f03992994c8642026147a2ef8014f0ce2c9564ab2a03dd04137d11942ad
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