49 lines
3.7 KiB
Markdown
49 lines
3.7 KiB
Markdown
# SwinTransformer
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---
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## 目录
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* [1. 概述](#1)
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* [2. 精度、FLOPS 和参数量](#2)
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* [3. 基于V100 GPU 的预测速度](#3)
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<a name='1'></a>
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## 1. 概述
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Swin Transformer 是一种新的视觉 Transformer 网络，可以用作计算机视觉领域的通用骨干网路。SwinTransformer 由移动窗口（shifted windows）表示的层次 Transformer 结构组成。移动窗口将自注意计算限制在非重叠的局部窗口上，同时允许跨窗口连接，从而提高了网络性能。[论文地址](https://arxiv.org/abs/2103.14030)。
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<a name='2'></a>
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## 2. 精度、FLOPS 和参数量
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Params<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 0.812 | 0.955 | 4.5 | 28 |
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| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 0.832 | 0.962 | 8.7 | 50 |
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| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 0.835 | 0.965 | 15.4 | 88 |
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| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 0.845 | 0.970 | 47.1 | 88 |
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| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 0.852 | 0.975 | 15.4 | 88 |
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| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 0.864 | 0.980 | 47.1 | 88 |
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| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 0.863 | 0.979 | 34.5 | 197 |
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| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 0.873 | 0.982 | 103.9 | 197 |
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[1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
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**注**：与 Reference 的精度差异源于数据预处理不同。
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<a name='3'></a>
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## 3. 基于 V100 GPU 的预测速度
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| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
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| ------------------------------------------------------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
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| SwinTransformer_tiny_patch4_window7_224 | 224 | 256 | 6.59 | 9.68 | 16.32 |
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| SwinTransformer_small_patch4_window7_224 | 224 | 256 | 12.54 | 17.07 | 28.08 |
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| SwinTransformer_base_patch4_window7_224 | 224 | 256 | 13.37 | 23.53 | 39.11 |
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| SwinTransformer_base_patch4_window12_384 | 384 | 384 | 19.52 | 64.56 | 123.30 |
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| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 224 | 256 | 13.53 | 23.46 | 39.13 |
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| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 384 | 384 | 19.65 | 64.72 | 123.42 |
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| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 224 | 256 | 15.74 | 38.57 | 71.49 |
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| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 384 | 384 | 32.61 | 116.59 | 223.23 |
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[1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
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