Instructions to use prithivMLmods/Weather-Image-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Weather-Image-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Weather-Image-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/Weather-Image-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Weather-Image-Classification") - Inference
- Notebooks
- Google Colab
- Kaggle
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# Weather-Image-Classification
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> Weather-Image-Classification is a vision-language model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. It is trained to recognize weather conditions from images using the SiglipForImageClassification architecture.
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* Automated weather tagging for photography and media.
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* Enhancing dataset labeling in weather-related research.
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* Supporting smart surveillance and traffic systems.
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* Improving scene understanding in autonomous vehicles.
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# Weather-Image-Classification
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> Weather-Image-Classification is a vision-language model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. It is trained to recognize weather conditions from images using the SiglipForImageClassification architecture.
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* Automated weather tagging for photography and media.
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* Enhancing dataset labeling in weather-related research.
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* Supporting smart surveillance and traffic systems.
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* Improving scene understanding in autonomous vehicles.
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