Instructions to use hf-internal-testing/tiny-random-SegformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegformerModel 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-SegformerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-SegformerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SegformerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0c529be402fc36569dc204a92af4ec905b5866a80b245dfd05a8fc883b95de3
- Size of remote file:
- 3.01 MB
- SHA256:
- be01e75368c53298b3422414f37bd033f3e73d44f84b35dd2f7d6dfc2b0c5379
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.