Instructions to use ivensamdh/swinv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivensamdh/swinv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ivensamdh/swinv2") 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("ivensamdh/swinv2") model = AutoModelForImageClassification.from_pretrained("ivensamdh/swinv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce018d11552210b01fb3e42045733047550479e4119144f3bb214cddba650078
- Size of remote file:
- 347 MB
- SHA256:
- 3b2211673825e0a2f5b17fdd500cf26e2e52d8bcbb6057d84f080a9fca431a95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.