Instructions to use hf-tiny-model-private/tiny-random-BeitForSemanticSegmentation 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-BeitForSemanticSegmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, BeitForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-BeitForSemanticSegmentation") model = BeitForSemanticSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-BeitForSemanticSegmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf4ed5cad71b5dac58622ae72c7458987b671e4c3f7417deb8451973ea3cb7e3
- Size of remote file:
- 991 kB
- SHA256:
- b3e6c3f48a3cad52238d9bd7b5babece476ccd87b1d7770ce0b3163af370a06e
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