Instructions to use prithivMLmods/Fire-Risk-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Fire-Risk-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Fire-Risk-Detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Fire-Risk-Detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Fire-Risk-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2198/rng_state.pth from prithivMLmods/Fire-Risk-Detection: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/prithivMLmods/Fire-Risk-Detection/resolve/main/checkpoint-2198/rng_state.pth
- Command line
-
hf download hf://prithivMLmods/Fire-Risk-Detection/checkpoint-2198/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/prithivMLmods/Fire-Risk-Detection/resolve/main/checkpoint-2198/rng_state.pth
14.2 kB
- Xet hash:
- 5ba3832697c7f72660d6c510f2319b15726fa9cb26d499fe77959e0760d7c8f0
- Size of remote file:
- 14.2 kB
- SHA256:
- 1f7ea8883c164178080363baee98398c7a8d1e1a522fd334729e8f0d56e1b69a
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