Instructions to use Dhika/raildefect6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect6") 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/raildefect6") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7344f2a51fa8033ffa9b4f9941f0a98104afb9f5207bfab96e8b7ccab173197
- Size of remote file:
- 3.9 kB
- SHA256:
- c3859c40c7c2f4705b52c37a7c082a57057263a2e1ecde60d7fd7e137f12fce8
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