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
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README.md
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license: apache-2.0
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license: apache-2.0
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---
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```py
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Classification Report:
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precision recall f1-score support
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high-traffic 0.8647 0.8410 0.8527 585
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low-traffic 0.8778 0.9485 0.9118 3803
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medium-traffic 0.7785 0.6453 0.7057 1187
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no-traffic 0.8730 0.7292 0.7946 528
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accuracy 0.8602 6103
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macro avg 0.8485 0.7910 0.8162 6103
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weighted avg 0.8568 0.8602 0.8559 6103
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```
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