Instructions to use tiya1012/vit-accident-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/vit-accident-image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tiya1012/vit-accident-image") 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("tiya1012/vit-accident-image") model = AutoModelForImageClassification.from_pretrained("tiya1012/vit-accident-image", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/vit-accident-image: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/tiya1012/vit-accident-image/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/vit-accident-image/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/vit-accident-image/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- fc993198e9954f06a87a6cd8f683267a157b1cd10f3c52dfc48615bf868270b2
- Size of remote file:
- 343 MB
- SHA256:
- 7454ee625be062cb0239a1b7358f0dde24bfabd40b9e7a35b6b0a65e7125fc1e
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