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:
- fb8d01dc58629b7430be25df4e1818fae62aac27ef84f43f5eb2dcac000c94bf
- Size of remote file:
- 343 MB
- SHA256:
- bc7108d9248d61e2c1ad4e0af3440e83e6718d072c49e4f41ca05d294cd63e54
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