Instructions to use Dhika/raildefect5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect5") 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/raildefect5") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 834025b9ab4791bc9a9314bc74e53d203dd7932ce1188c1cff382ed13686c24d
- Size of remote file:
- 3.9 kB
- SHA256:
- 8eafd77f67a403195b7f8713652eb8b660f9828993f6ef047655183bb6061a68
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