Instructions to use gagan3012/swinv2-base-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gagan3012/swinv2-base-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gagan3012/swinv2-base-512") 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("gagan3012/swinv2-base-512") model = AutoModelForImageClassification.from_pretrained("gagan3012/swinv2-base-512") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd7c044a4be4ba89279f6c1453be4b5fd0758e185b7f0e66a6aef5e92b14ab32
- Size of remote file:
- 348 MB
- SHA256:
- f83d2132f8cf168f146a348dd016e750e69ed0fe46f45566a8d9ec160c8ad028
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