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Update README.md
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HantingChen authored Jul 31, 2023
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Expand Up @@ -27,23 +27,24 @@ VanillaNet achieves comparable performance to prevalent computer vision foundati
- **13 layers'** VanillaNet (1.5x*) achieves about **83%** Top-1 accuracy with **9.72ms**, over **100%** speed increase compared to Swin-S (**20.25ms**).
- With tensorRT FP32 on A100, **11 layers'** VanillaNet achieves about **81%** Top-1 accuracy with **0.69ms**, over **100%** speed increase compared to Swin-T (**1.41ms**) and ResNet-101 (**1.58ms**).

| name | Params(M) | FLOPs(B) | Acc(%) | Latency(ms) <br/>Pytorch <br/>A100 | Latency(ms) <br/>MindSpore <br/>Ascend 910 | Latency(ms) <br/>TRT FP32 <br/>A100 | Latency(ms) <br/>TRT FP16 <br/>A100 |
|:---:|:---:|:---:|:---:| :---:|:---:|:---:|:---:|
| Swin-T | 28.3 | 4.5 | 81.18 | 10.51 | 2.24 | 1.41 | 0.98 |
| ResNet-18 | 11.7 | 1.8 | 70.6 | 3.12 | 0.60 | 0.41 | 0.28 |
| ResNet-34 |21.8|3.7|75.5|5.57|0.97|0.77|0.49|
| ResNet-50 |25.6|4.1|79.8|7.64|1.23|0.80|0.54|
| ResNet-101 |45.0|8.0|81.3|15.35|2.34|1.58|1.04|
| ResNet-152 |60.2|11.5|81.8|22.19|3.40|2.30|1.49|
| **VanillaNet-5** | 15.5 | 5.2 | 72.49 | 1.61 |0.39|0.33|0.27|
| **VanillaNet-6** | 32.5 | 6.0 | 76.36 | 2.01 |0.53|0.40|0.33|
| **VanillaNet-7** | 32.8 | 6.9 | 77.98 | 2.27 |0.76|0.47|0.39|
| **VanillaNet-8** | 37.1 | 7.7 | 79.13 | 2.56 |0.80|0.52|0.45|
| **VanillaNet-9** | 41.4 | 8.6 | 79.87 | 2.91 |0.86|0.58|0.49|
| **VanillaNet-10** | 45.7 | 9.4 | 80.57 | 3.24 |0.89|0.63|0.53|
| **VanillaNet-11** | 50.0 | 10.3 | 81.08 | 3.59 | 0.95 |0.69|0.58|
| **VanillaNet-12** | 54.3 | 11.1 | 81.55 | 3.82 |1.00|0.75|0.62|
| **VanillaNet-13** | 58.6 | 11.9 | 82.05 | 4.26 |1.05|0.82|0.67|
| name | Params(M) | FLOPs(B) | Acc(%) | Latency(ms) <br/>Pytorch <br/>A100 | Latency(ms) <br/>MindSpore <br/>Ascend 910 | Latency(ms) <br/>TRT FP32 <br/>A100 | Latency(ms) <br/>TRT FP16 <br/>A100 | Latency(ms) <br/>TRT FP16 <br/>V100 |
|:---:|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|
| Swin-T | 28.3 | 4.5 | 81.18 | 10.51 | 2.24 | 1.41 | 0.98 | 2.83 |
| ConvNextV2-N | 15.6 | 2.45 | 81.2 | 6.85 | 3.43 | - | - | 2.27 |
| ResNet-18 | 11.7 | 1.8 | 70.6 | 3.12 | 0.60 | 0.41 | 0.28 | 0.55 |
| ResNet-34 |21.8|3.7|75.5|5.57|0.97|0.77|0.49| 0.95 |
| ResNet-50 |25.6|4.1|79.8|7.64|1.23|0.80|0.54| 1.24 |
| ResNet-101 |45.0|8.0|81.3|15.35|2.34|1.58|1.04| 1.86 |
| ResNet-152 |60.2|11.5|81.8|22.19|3.40|2.30|1.49| 3.06 |
| **VanillaNet-5** | 15.5 | 5.2 | 72.49 | 1.61 |0.39|0.33|0.27| 0.61 |
| **VanillaNet-6** | 32.5 | 6.0 | 76.36 | 2.01 |0.53|0.40|0.33|0.66|
| **VanillaNet-7** | 32.8 | 6.9 | 77.98 | 2.27 |0.76|0.47|0.39|0.71|
| **VanillaNet-8** | 37.1 | 7.7 | 79.13 | 2.56 |0.80|0.52|0.45|0.79|
| **VanillaNet-9** | 41.4 | 8.6 | 79.87 | 2.91 |0.86|0.58|0.49|0.91|
| **VanillaNet-10** | 45.7 | 9.4 | 80.57 | 3.24 |0.89|0.63|0.53|1.01|
| **VanillaNet-11** | 50.0 | 10.3 | 81.08 | 3.59 | 0.95 |0.69|0.58|1.20|
| **VanillaNet-12** | 54.3 | 11.1 | 81.55 | 3.82 |1.00|0.75|0.62|1.38|
| **VanillaNet-13** | 58.6 | 11.9 | 82.05 | 4.26 |1.05|0.82|0.67|1.39|

## Downstream Tasks

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