Methods › General › Network Shrinking › TinyNet
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
TinyNet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions. This paper aims to explore the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs. Different from the network enlarging, we observe that resolution and depth are more important than width for tiny networks. Therefore, the original method, i.e., the compound scaling in EfficientNet is no longer suitable. To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint. Experimental results on the ImageNet benchmark illustrate that our TinyNet performs much better than the smaller version of EfficientNets using the inversed giant formula. For instance, our TinyNet-E achieves a 59.9% Top-1 accuracy with only 24M FLOPs, which is about 1.9% higher than that of the previous best MobileNetV3 with similar computational cost.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Image Classification with Classic and Deep Learning Techniques 11 May 2021 · 1 repository · arXiv:2105.04895
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Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets 1 Dec 2020 · 3 repositories
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Effective Model Compression via Stage-wise Pruning 10 Nov 2020 · 0 repositories · arXiv:2011.04908
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Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets 28 Oct 2020 · 9 repositories · arXiv:2010.14819Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Classification | 3 |
| Classification | 1 |
| Deep Learning | 1 |
| Model Compression | 1 |
| Rubik's Cube | 1 |
| image-classification | 1 |
| model | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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