Instructions to use RationAI/LSP-DETR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RationAI/LSP-DETR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="RationAI/LSP-DETR", trust_remote_code=True)# Load model directly from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained("RationAI/LSP-DETR", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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# LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images
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LSP-DETR (Local Star Polygon DEtection TRansformer) is a lightweight, efficient, and end-to-end deep learning model for nuclei instance segmentation in histopathological images. It combines a DETR-based transformer decoder with star-convex polygon shape descriptors to enable accurate and fast segmentation without complex post-processing.
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# LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images
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[GitHub](https://github.com/RationAI/lsp-detr)
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LSP-DETR (Local Star Polygon DEtection TRansformer) is a lightweight, efficient, and end-to-end deep learning model for nuclei instance segmentation in histopathological images. It combines a DETR-based transformer decoder with star-convex polygon shape descriptors to enable accurate and fast segmentation without complex post-processing.
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