Instructions to use edixo/road_good_damaged_condition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edixo/road_good_damaged_condition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="edixo/road_good_damaged_condition") 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("edixo/road_good_damaged_condition") model = AutoModelForImageClassification.from_pretrained("edixo/road_good_damaged_condition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34477229855ea173b1e7cfeb852503b38b5a75e9388b5351bac2fbd2e364ee37
- Size of remote file:
- 343 MB
- SHA256:
- e0af77cd8a062a13be8e46db8173fa4d4678afe6e6862efe85a03e1a6097c6e1
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