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:
- d543f3567a8ba432a2134442e808c9ce298f2bffadb377c031fa4a03a48ae498
- Size of remote file:
- 343 MB
- SHA256:
- 9098f5f1b621dc9fe686f6e9921a73cc135948e9f9ba2d416a2e26ec40242cd5
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