Instructions to use Gurveer05/vit-base-patch16-224-in21k-fire-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gurveer05/vit-base-patch16-224-in21k-fire-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Gurveer05/vit-base-patch16-224-in21k-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("Gurveer05/vit-base-patch16-224-in21k-fire-detection") model = AutoModelForImageClassification.from_pretrained("Gurveer05/vit-base-patch16-224-in21k-fire-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
The purpose of this model is to detect wildfires / forest fires in satellite imagery for early wildfire detection. It has been trained on a custom dataset from kaggle.
Outputs are encoded as follows:
- LABEL_0: Fire
- LABEL_1: No Fire
References:
Kaggle Notebook(Dataset in input)Github Repository with website integration
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