Instructions to use EdBianchi/vit-fire-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdBianchi/vit-fire-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="EdBianchi/vit-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("EdBianchi/vit-fire-detection") model = AutoModelForImageClassification.from_pretrained("EdBianchi/vit-fire-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 993a4e6852b1ea21c55c58177e305b2793868e872dbfe52c9162c3709808faa6
- Size of remote file:
- 3.45 kB
- SHA256:
- b8e9f83327d210db1f8756e367f7f91f99ba46b7161c53b9ee279f4af06b6414
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.