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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b47afce4a9887c9ad9a9b0d2c20cb80d27fbd4b609bfa9a212b89dbf4adaa89
- Size of remote file:
- 687 MB
- SHA256:
- 53216e07ec9d94910fff983c20e9ffee851190a86e9c329b3a389a4afbb7b148
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