Papers › Lets keep it simple, Using simple architectures to outperform deeper and more complex...

Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

22 Aug 2016arXiv:1608.06037archive 2025-07-28

Seyyed Hossein Hasanpour, Mohammad Rouhani, Mohsen Fayyaz, Mohammad Sabokrou

Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet, ResNet, GoogleNet, include tens to hundreds of millions of parameters, which impose considerable computation and memory overhead. This limits their practical use for training, optimization and memory efficiency. On the contrary, light-weight architectures, being proposed to address this issue, mainly suffer from low accuracy. These inefficiencies mostly stem from following an ad hoc procedure. 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. This makes it very handy for embedded systems or systems with computational and memory limitations. We achieved state-of-the-art result on CIFAR10 outperforming several heavier architectures, near state of the art on MNIST and competitive results on CIFAR100 and SVHN. We also outperformed the much larger and deeper architectures such as VGGNet and popular variants of ResNets among others on the ImageNet dataset. Models are made available at: https://github.com/Coderx7/SimpleNet

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Coderx7/SimpleNet officialmentioned in papermentioned on GitHubcaffe2MIT report
Coderx7/SimpleNet_Pytorch mentioned on GitHubpytorch report
JavierAntoran/moby_dick mentioned on GitHubpytorchMIT report
JavierAntoran/moby_dick_whale_detection mentioned on GitHubpytorchMIT report
dorlivne/simple_net_pruning mentioned on GitHubtf report
luckymouse0/SimpleNet-TF mentioned on GitHubtfMIT report
pdoyle5000/simple_net mentioned on GitHubpytorch report
zhuoyang125/simple_classifier mentioned on GitHubtf report

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simplenet Coderx7/SimpleNet_Pytorch/cifar/models/simplenet.py community (archive-listed) ran · our draft was wrong MIT (permissive) · a4e6e7d660e9dc5f · report
simplenet_cifar_310k Coderx7/SimpleNet_Pytorch/cifar/models/simplenet.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 76760ce0a901a24a · report
simplenet_cifar_460k Coderx7/SimpleNet_Pytorch/cifar/models/simplenet.py community (archive-listed) ran · our draft was wrong MIT (permissive) · d7ced00ade152828 · report
gen_whalefb_mtx JavierAntoran/moby_dick/HMM_solution/format_data_wavelets_dct.py community (archive-listed) unverified MIT (permissive) · 05a762a121e5ad7d · report
gen_whalefb_mtx JavierAntoran/moby_dick/NN_solution/format_data_250ms.py community (archive-listed) unverified MIT (permissive) · e9fc42cebc176243 · report
get_delta_mtx JavierAntoran/moby_dick/NN_solution/format_data_25ms.py community (archive-listed) unverified MIT (permissive) · 6bb7eb96c72778f7 · report
get_mtx_deltas JavierAntoran/moby_dick/HMM_solution/format_data_wavelets_dct.py community (archive-listed) unverified MIT (permissive) · 90e483b5f738c3e2 · report
image_statistics JavierAntoran/moby_dick/template_boosting_solution/generate_templates.py community (archive-listed) unverified MIT (permissive) · 07abbcfa131c3e53 · report
windower JavierAntoran/moby_dick/HMM_solution/format_data_wavelets_dct.py community (archive-listed) unverified MIT (permissive) · 2cb25e763e9871bb · report
windower JavierAntoran/moby_dick/NN_solution/format_data_250ms.py community (archive-listed) unverified MIT (permissive) · e228c13c8c5d69c2 · report
windower JavierAntoran/moby_dick/NN_solution/format_data_25ms.py community (archive-listed) unverified MIT (permissive) · 548cfa2028318949 · report

Tasks

Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification CIFAR-10 SimpleNetv1 Percentage correct 95.51 #132 of 265 Archive leaderboard report
Image Classification CIFAR-100 SimpleNetv1 Percentage correct 78.37 #138 of 211 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-9m-correct-labels Number of params 9.5M #652 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-9m-correct-labels Top 1 Accuracy 81.24 #652 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-5m-correct-labels Number of params 5.7M #773 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-5m-correct-labels Top 1 Accuracy 79.12 #773 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-075-correct-labels Number of params 3M #941 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-075-correct-labels Top 1 Accuracy 75.66 #941 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-9m Number of params 9.5M #978 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-9m Top 1 Accuracy 74.17 #978 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-5m Number of params 5.7M #1004 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-5m Top 1 Accuracy 71.94 #1004 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-05-correct-labels Number of params 1.5M #1030 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-05-correct-labels Top 1 Accuracy 69.11 #1030 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-075 Number of params 3M #1035 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-075 Top 1 Accuracy 68.15 #1035 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-05 Number of params 1.5M #1051 of 1060 Archive leaderboard report
Image Classification ImageNet SimpleNetV1-small-05 Top 1 Accuracy 61.52 #1051 of 1060 Archive leaderboard report
Image Classification MNIST SimpleNetv1 Percentage error 0.25 #11 of 81 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: SimpleNet

1x1 ConvolutionBatch NormalizationConvolutionMax PoolingReLUSGDSimpleNetSoftmaxWeight Decay

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