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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fe70a27ed68225a232ffe80c19469894881852f0ff1ef7582e242131aa35920
- Size of remote file:
- 343 MB
- SHA256:
- 4135dfa146b4361269b47759fe825765a7365cb2bc09f7330f75a9341d3864f0
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