Instructions to use Dhika/raildefect8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect8") 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/raildefect8") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 561f72ba59833acc927bd044ca267dec93e3e07db12d492ac420f502a77c9a36
- Size of remote file:
- 3.9 kB
- SHA256:
- f31cd57d5bb49dca79a7c9c51d9900cbf740880c3d39ff745dbeaf537350407a
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