Instructions to use vigneshgs7/segformer-b2-p142-cvat-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vigneshgs7/segformer-b2-p142-cvat-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="vigneshgs7/segformer-b2-p142-cvat-2")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("vigneshgs7/segformer-b2-p142-cvat-2") model = SegformerForSemanticSegmentation.from_pretrained("vigneshgs7/segformer-b2-p142-cvat-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97042b76a3b57547e23998f1a510035db05d3f5f59ea7c30a407799c2b021479
- Size of remote file:
- 4.6 kB
- SHA256:
- 2859bf9ba337bddfa6ca3c1deea9df978d9af2c0e6af519afef309893832b318
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