Instructions to use ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching") 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("ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching") model = AutoModelForImageClassification.from_pretrained("ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ArwaAbdul/swinv2-tiny-patch4-window8-datasetinriching/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- 71707da9555031475b126d38e84501339b1e3a64f77e3a06d5e69d6bae519742
- Size of remote file:
- 110 MB
- SHA256:
- f0614d90900643467b59dd37ec9938b5ef85e74e5dd72fe22bfd7ba6296b5d20
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