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:
- 390cb794ece8eb34b8c5863437a3f2eac667bd20c06a5b2c5b1dc8fcb8d176be
- Size of remote file:
- 343 MB
- SHA256:
- 6418d31072de403ffd87154cb8b2499eccc59e007b92a112bd7841453ba0fe0f
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