University of Michigan - Ann Arbor

On the ImageNet 2012 classification benchmark (1,000 classes, 1.28 million training images, and 50k validation images), scaling depth with residual bottleneck networks produces substantial, monotonic accuracy gains without exhibiting degradation. In 10-crop evaluation on the validation set, top-1 and top-5 error rates steadily decrease as depth scales up:

- ResNet-50: 22.85% top-1 error, 6.71% top-5 error
- ResNet-101: 21.75% top-1 error, 6.05% top-5 error
- ResNet-152: 21.43% top-1 error, 5.71% top-5 error

Under single-model evaluation using dense multi-scale testing, ResNet-152 achieves a top-5 validation error of 4.49% (and 19.38% top-1 error). This standalone model outperforms previous state-of-the-art ensemble systems, including GoogLeNet (6.66% test top-5), VGG ensembles (7.32% test top-5), and BN-inception (4.82% test top-5).

Combining six models of varying depths (incorporating only two 152-layer models at the submission deadline) forms an ensemble that achieves a 3.57% top-5 error on the ImageNet evaluation test set. This performance earned first place in the ILSVRC 2015 classification competition.

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