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:
- f32d8cdc79f1161f68dcbc882f75e24a79740b3beae0b6b4c5f4d5322a7041fd
- Size of remote file:
- 343 MB
- SHA256:
- 40cecfc6a922c983cc19c3d137efd1a11d4ecdf2bc7e62efbb04ec81aafb7688
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