Instructions to use jtsmalling/cv_crashcourse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtsmalling/cv_crashcourse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jtsmalling/cv_crashcourse") 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("jtsmalling/cv_crashcourse") model = AutoModelForImageClassification.from_pretrained("jtsmalling/cv_crashcourse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from jtsmalling/cv_crashcourse: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/jtsmalling/cv_crashcourse/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://jtsmalling/cv_crashcourse@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/jtsmalling/cv_crashcourse/resolve/refs%2Fpr%2F2/optimizer.pt
687 MB
- Xet hash:
- 7ab7f35f8e0ea85bb9ef29b2a8a11f49aa1940cfe10f1819a0e86fe892e068fd
- Size of remote file:
- 687 MB
- SHA256:
- d174f94cc1c77208b34c896907a42ef8d44a1ff9e375238437c970fd22099e4a
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