Instructions to use microsoft/cvt-13-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/cvt-13-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/cvt-13-384") 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("microsoft/cvt-13-384") model = AutoModelForImageClassification.from_pretrained("microsoft/cvt-13-384") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f9208b04a9c646bcb7b38723cb653389285fab872d8e1de5ba42c12412b722e
- Size of remote file:
- 80.3 MB
- SHA256:
- bac7a58899c41b4cd90bfded50f1d4e1cf283c2d3255e3fce7ff7350e0d23438
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.