Instructions to use Davidals/fire-image-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davidals/fire-image-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Davidals/fire-image-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Davidals/fire-image-classifier") model = AutoModelForImageClassification.from_pretrained("Davidals/fire-image-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Davidals/fire-image-classifier: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Davidals/fire-image-classifier/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Davidals/fire-image-classifier@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Davidals/fire-image-classifier/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- 23382637f788ee3d15f30c6ef8a90944adbd2edf6e3c2e702db8769d53784517
- Size of remote file:
- 343 MB
- SHA256:
- d2a6350ed2369c61a26490bfc7cff339265744fa43d7b7780553a74a053f05ef
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