Instructions to use tzhao3/vit-GTSRB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-GTSRB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-GTSRB") 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("tzhao3/vit-GTSRB") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-GTSRB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tzhao3/vit-GTSRB: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/tzhao3/vit-GTSRB/resolve/main/optimizer.pt
- Command line
-
hf download hf://tzhao3/vit-GTSRB/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tzhao3/vit-GTSRB/resolve/main/optimizer.pt
687 MB
- Xet hash:
- 5c3422aab7c51ecd53c61a4d978c6e504cf4fc0f65157b00fed61cd1a5ecdd91
- Size of remote file:
- 687 MB
- SHA256:
- fdb672723ff2b79d087e55e05e15a4323588232d988c5dab9d93ebcfbf986bd9
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