Instructions to use hf-tiny-model-private/tiny-random-Swinv2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Swinv2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-Swinv2ForImageClassification") 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("hf-tiny-model-private/tiny-random-Swinv2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c60be8025346e4139211507032679ab731d9904520249ffdd9902d50596ceb21
- Size of remote file:
- 310 kB
- SHA256:
- 13bc0727bc7c95f5db5decf5d90e7a10e28b7bc1c9f4211ad592a9ce9da16596
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