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:
- 78633c107d3a605c3a9da9676031f6a997453c284c6906bf3d05ae8826caeef6
- Size of remote file:
- 687 MB
- SHA256:
- 19738c04dc143800fdb1672e92947446707aa5f2443c8ce02457ea8ab86e2914
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