Instructions to use hf-internal-testing/tiny-random-Swinv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swinv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-Swinv2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swinv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Swinv2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 879db254952ea5c836fd91c294f6f96241f02f43276876ea428b9b9a6afd8950
- Size of remote file:
- 309 kB
- SHA256:
- bff28ce8fb01fdef88e9424a08e4ffe5f4ccf9f8de7cda5270d7f9a6553cec16
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