We propose a simple architecture, called SimpleNet, based on a set of designing principles, with which we empirically show, a well-crafted yet simple and reasonably deep architecture can perform on par with deeper and more complex architectures. SimpleNet provides a good tradeoff between the computation/memory efficiency and the accuracy. Our simple 13-layer architecture outperforms most of the deeper and complex architectures to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks while having 2 to 25 times fewer number of parameters and operations.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Classification||CIFAR-10||SimpleNetv1||Percentage correct||95.51||# 11|
|Image Classification||CIFAR-100||SimpleNetv1||Percentage correct||78.37||# 6|
|Image Classification||MNIST||SimpleNetv1||Percentage error||0.2||# 2|