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Convolutional neural networks typically encode an input image into a seriesof intermediate features with decreasing resolutions. While this structure issuited to classification tasks, it does not perform well for tasks requiringsimultaneous recognition and localization (e.g., object detection). Theencoder-decoder architectures are proposed to resolve this by applying adecoder network onto a backbone model designed for classification tasks. Inthis paper, we argue encoder-decoder architecture is ineffective in generatingstrong multi-scale features because of the scale-decreased backbone. We proposeSpineNet, a backbone with scale-permuted intermediate features and cross-scaleconnections that is learned on an object detection task by Neural ArchitectureSearch. Using similar building blocks, SpineNet models outperform ResNet-FPNmodels by ~3% AP at various scales while using 10-20% fewer FLOPs. Inparticular, SpineNet-190 achieves 52.5% AP with a MaskR-CNN detector andachieves 52.1% AP with a RetinaNet detector on COCO for a single model withouttest-time augmentation, significantly outperforms prior art of detectors.SpineNet can transfer to classification tasks, achieving 5% top-1 accuracyimprovement on a challenging iNaturalist fine-grained dataset. Code is at:https://github.com/tensorflow/tpu/tree/master/models/official/detection.

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization