Instructions to use Dhika/raildefect6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect6") 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/raildefect6") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e5b8bd4540429c77bd495777a79d7435ffbbab7b1d62cfcd2c103b4701a9d66
- Size of remote file:
- 343 MB
- SHA256:
- 695d8ecd99576e11aab677a08e3123ae4f46bbdcf658ec977ffdcf9cf8f20e7e
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