Visualization of the MobileNet-v3-large shapes (ordered similarly by decreasing time percentage, so the most important shapes come first).
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January 17, 2021 17:44
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Matmul shapes in MobileNet-v3-large, EfficientNet-Lite2 and EfficientNet-B4, 8bit quantized, by decreasing CPU time % on Pixel4
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| Note: EfficientNet-B4 is a huge model, unlikely to be deployed in any application except ones that would require the highest possible accuracy. | |
| Still, it is currently an optimal trade-off point on the model-accuracy-vs-cost curves. | |
| * 4.95% matmul shape: 1632x272x49 | |
| * 4.63% matmul shape: 960x160x196 | |
| * 4.10% matmul shape: 192x32x3136 | |
| * 3.81% matmul shape: 272x1632x49 | |
| * 3.19% matmul shape: 672x112x196 | |
| * 2.95% matmul shape: 144x24x12544 | |
| * 2.79% matmul shape: 160x960x196 | |
| * 2.42% matmul shape: 336x56x784 | |
| * 1.73% matmul shape: 32x192x3136 | |
| * 1.57% matmul shape: 2688x448x49 | |
| * 1.44% matmul shape: 48x27x12544 | |
| * 1.41% matmul shape: 112x672x196 | |
| * 1.22% matmul shape: 448x2688x49 | |
| * 1.06% matmul shape: 24x48x12544 | |
| * 1.06% matmul shape: 1792x448x49 | |
| * 1.04% matmul shape: 24x24x12544 | |
| * 0.96% matmul shape: 448x1632x49 | |
| * 0.67% matmul shape: 56x336x784 | |
| * 0.67% matmul shape: 32x144x3136 | |
| * 0.29% matmul shape: 160x672x196 | |
| * 0.16% matmul shape: 112x336x196 | |
| * 0.13% matmul shape: 272x960x49 | |
| * 0.08% matmul shape: 56x192x784 |
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| EfficientNet-Lite2 is a more reasonably sized model compared to EfficientNet-B4, while having higher accuracy than MobileNet-v3-large. It could be used in applications demanding high accuracy. | |
| * 6.10% matmul shape: 144x24x4225 | |
| * 5.49% matmul shape: 96x16x16900 | |
| * 5.40% matmul shape: 720x120x289 | |
| * 5.31% matmul shape: 1248x208x81 | |
| * 3.33% matmul shape: 208x1248x81 | |
| * 3.33% matmul shape: 288x48x1089 | |
| * 3.10% matmul shape: 32x27x16900 | |
| * 3.05% matmul shape: 528x88x289 | |
| * 2.82% matmul shape: 120x720x289 | |
| * 2.39% matmul shape: 24x144x4225 | |
| * 2.07% matmul shape: 16x32x16900 | |
| * 1.74% matmul shape: 1280x352x81 | |
| * 1.55% matmul shape: 88x528x289 | |
| * 1.55% matmul shape: 48x288x1089 | |
| * 1.27% matmul shape: 352x1248x81 | |
| * 0.80% matmul shape: 208x720x81 | |
| * 0.70% matmul shape: 24x96x4225 | |
| * 0.66% matmul shape: 120x528x289 | |
| * 0.66% matmul shape: 48x144x1089 | |
| * 0.38% matmul shape: 88x288x289 |
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| Note: MobiletNet-v3-large is a typical model to deploy in current applications thanks to its moderate size. | |
| * 7.63% matmul shape: 64x16x12544 | |
| * 4.35% matmul shape: 672x112x196 | |
| * 4.18% matmul shape: 16x27x12544 | |
| * 4.18% matmul shape: 960x160x49 | |
| * 4.15% matmul shape: 72x24x3136 | |
| * 3.76% matmul shape: 16x16x12544 | |
| * 2.08% matmul shape: 120x40x784 | |
| * 1.91% matmul shape: 24x72x3136 | |
| * 1.71% matmul shape: 160x960x49 | |
| * 1.68% matmul shape: 240x40x784 | |
| * 1.57% matmul shape: 480x80x196 | |
| * 1.46% matmul shape: 40x120x784 | |
| * 1.35% matmul shape: 24x64x3136 | |
| * 1.12% matmul shape: 112x480x196 | |
| * 1.12% matmul shape: 112x672x196 | |
| * 0.81% matmul shape: 184x80x196 | |
| * 0.73% matmul shape: 200x80x196 | |
| * 0.70% matmul shape: 160x672x49 | |
| * 0.56% matmul shape: 80x200x196 | |
| * 0.56% matmul shape: 80x184x196 | |
| * 0.53% matmul shape: 40x72x784 | |
| * 0.53% matmul shape: 80x240x196 |
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