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-4396/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-4396/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Fire-Risk-Detection/checkpoint-4396/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Fire-Risk-Detection/resolve/main/checkpoint-4396/optimizer.pt
687 MB
- Xet hash:
- 988ef0669621559493421a0b7b89ae01f16c510740c068cca5c6d5b7c8b0ae9e
- Size of remote file:
- 687 MB
- SHA256:
- 5c7dad23017f9b67247fff6781aebfddf8b84a6b449b067e527cb2d7e4f314d5
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