Instructions to use prithivMLmods/Traffic-Density-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Traffic-Density-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Traffic-Density-Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Traffic-Density-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Traffic-Density-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80847683f9d4e7f5266132a3c13b650bc79df71585c18e3b48e4ab561ee23029
- Size of remote file:
- 687 MB
- SHA256:
- 0ed9582c42a64a0ab30720e4cdf77c62b4128e232c147ab582e8a435589e43b4
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