Instructions to use Dhika/raildefect3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect3") 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/raildefect3") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc5b961ab697aab415b28caa1d274545ac38bc20bef69b76ea1d9f6249c2aa65
- Size of remote file:
- 343 MB
- SHA256:
- 9b26f80d70d86d17621f11c92f20090bb357577a396c8f72d97a7e8be78c9540
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