mlsanityData to 14 Oct 2026

DaCapo: a modular deep learning framework for scalable 3D image segmentation

arXiv 2408.02834 ↗First posted 5 Aug 2024cs.CV cs.LG eess.IVcited by 1

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Abstract (arXiv)

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 9 references that are in the map. First posted on arXiv 5 Aug 2024.

Builds on

2 of 9 references.

References · 2

  1. 2017Superhuman Accuracy on the SNEMI3D Connectomics Challengecited by 64
  2. 2024Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentationcited by 1

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