pytorch-image-models/docs/models/.templates/models/dla.md

Deep Layer Aggregation

Extending “shallow” skip connections, Dense Layer Aggregation (DLA) incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through an architectural framework, independent of the choice of backbone, for compatibility with current and future networks.

IDA focuses on fusing resolutions and scales while HDA focuses on merging features from all modules and channels. IDA follows the base hierarchy to refine resolution and aggregate scale stage-bystage. HDA assembles its own hierarchy of tree-structured connections that cross and merge stages to aggregate different levels of representation.

{% include 'code_snippets.md' %}

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

@misc{yu2019deep,
      title={Deep Layer Aggregation}, 
      author={Fisher Yu and Dequan Wang and Evan Shelhamer and Trevor Darrell},
      year={2019},
      eprint={1707.06484},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}