Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. We also present several new streamlined architectures for both residual and non-residual Inception networks.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Classification||ImageNet||Inception ResNet V2||Top 1 Accuracy||80.1%||# 8|
|Image Classification||ImageNet||Inception ResNet V2||Top 5 Accuracy||95.1%||# 8|