Instructions to use Dhika/raildefect2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect2") 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/raildefect2") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24b999130cf44839dc293c56004b398df11f89dd210e9fe77d0e03699a0f66bc
- Size of remote file:
- 3.9 kB
- SHA256:
- 2a6e6e83e0148103972220a0f53ffe71a17082f9940b359eb18035995fc8d570
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