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:
- 2f2286cd33427e5a4afa23f6d53014e7d3fee36b078a30dff6b738511431106c
- Size of remote file:
- 3.9 kB
- SHA256:
- 4f41df7919ac5747067b1ddaec0f42458d59e0c3e835ac17d77d1f3f0c97c41a
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