Instructions to use hf-tiny-model-private/tiny-random-Swinv2Model 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-Swinv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-Swinv2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c478535add98f02e676aaee09003a855a6f2b422c6bb14d1aa438feea7445968
- Size of remote file:
- 328 kB
- SHA256:
- 646192ee8d48146140d9f3039a451dbfd9ce1eea3305f080151d35f6adc6b829
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