Instructions to use 24f2000010/fire-detection-siglip2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 24f2000010/fire-detection-siglip2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="24f2000010/fire-detection-siglip2") 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("24f2000010/fire-detection-siglip2") model = AutoModelForImageClassification.from_pretrained("24f2000010/fire-detection-siglip2") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This is a model finetuned on google-siglip2 vision transformer on wildfire-images dataset to classify fire, smoke and normal images.
Metrics
| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| fire | 0.9940 | 0.9881 | 0.9911 |
| normal | 0.9892 | 0.9941 | 0.9916 |
| smoke | 0.9990 | 1.0000 | 0.9995 |
| Accuracy | 0.9941 | ||
| Macro Avg | 0.9941 | 0.9941 | 0.9941 |
| Weighted Avg | 0.9941 | 0.9941 | 0.9941 |
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