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
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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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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