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
Training in progress, step 160
Browse files
pytorch_model.bin
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runs/Jun17_08-45-15_f1109e8efc38/events.out.tfevents.1686991566.f1109e8efc38.7752.0
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