Instructions to use schrilax/semantic-seg-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schrilax/semantic-seg-model with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("schrilax/semantic-seg-model") model = SegformerForSemanticSegmentation.from_pretrained("schrilax/semantic-seg-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4942bbbef0f3d573eafbbd3150b3671483e1bda7c8e6bb3cebce8f4e0b174fef
- Size of remote file:
- 14.9 MB
- SHA256:
- b7ab4a2411f4ab1d7e7de03fe9cc1a342a251fd60b1b7a9e87a6b4b672cdfe6a
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