Instructions to use EdBianchi/vit-fire-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdBianchi/vit-fire-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="EdBianchi/vit-fire-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("EdBianchi/vit-fire-detection") model = AutoModelForImageClassification.from_pretrained("EdBianchi/vit-fire-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#9
by librarian-bot - opened
README.md
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metrics:
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- precision
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- recall
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model-index:
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- name: vit-fire-detection
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results: []
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metrics:
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- precision
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- recall
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base_model: google/vit-base-patch16-224-in21k
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model-index:
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- name: vit-fire-detection
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results: []
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