Uses : A comprehensive framework for object detection.
- 2D + 3D implementations of prevalent object detectors
- Modular and light-weight structure ensuring sharing of all processing steps (incl. backbone architecture) for comparability of models.
- Training with bounding box and/or pixel-wise annotations.
- Dynamic patching and tiling of 2D + 3D images (for training and inference)
- Weighted consolidation of box predictions across patch-overlaps, ensembles, and dimensions.
- Monitoring + evaluation simultaneously on object and patient level.
- 2D + 3D output visualizations.
- Integration of COCO mean average precision metric.
- Integration of MIC-DKFZ batch generators for extensive data augmentation.
- Easy modification to evaluation of instance segmentation and/or semantic segmentation.
Platform : Source code.
File formats :
Language/API : Python, PyTorch.
Documentation : Website.
Users : Advanced, Developer.
Access : Free, open source.