Instructions to use Prahas10/roof_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prahas10/roof_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prahas10/roof_classification") 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("Prahas10/roof_classification") model = AutoModelForImageClassification.from_pretrained("Prahas10/roof_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9e8324f458ee89f65edb6a2d21df6cae0f80384fd65ceb197d4bffc23b24aab
- Size of remote file:
- 350 MB
- SHA256:
- 1f66f0afa519f78c97b98b4cdc7040125aeebda2f3021878e1a6988aec059afa
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