Instructions to use Dhika/raildefectfft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefectfft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefectfft") 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/raildefectfft") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefectfft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f147f0755f0f9643d20ca3d8a819d3a0604faa606d3a2066a563c9200c3a067
- Size of remote file:
- 3.9 kB
- SHA256:
- b5ee6a505485eda0c5f40a865997646e0874bafee26c81846d876b416aa2bb1f
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