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/optimizer.pt from prithivMLmods/Fire-Risk-Detection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Fire-Risk-Detection/resolve/main/checkpoint-2198/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Fire-Risk-Detection/checkpoint-2198/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Fire-Risk-Detection/resolve/main/checkpoint-2198/optimizer.pt
687 MB
- Xet hash:
- b70a806c788fbe97e602ff610cc32d661c262629a884549643b60dd6f66cb261
- Size of remote file:
- 687 MB
- SHA256:
- 6f91323c2914267114842e3480f4ca3c1c9be012f6f05fadd5a660acb8f8ccc9
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