Instructions to use Subh775/Dis-Seg-Former with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subh775/Dis-Seg-Former with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Subh775/Dis-Seg-Former")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Subh775/Dis-Seg-Former", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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## Inference results:
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## Inference results:
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## ONNX Weights
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This repository now includes .onnx exported weights for optimized real-time inference at: [here](https://huggingface.co/Subh775/Dis-Seg-Former/blob/main/export/rfdetr-seg-nano.onnx)
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