diff --git a/docs/zh_CN/algorithm_introduction/ImageNet_models.md b/docs/zh_CN/algorithm_introduction/ImageNet_models.md
index 402c47c06..6d05078c9 100644
--- a/docs/zh_CN/algorithm_introduction/ImageNet_models.md
+++ b/docs/zh_CN/algorithm_introduction/ImageNet_models.md
@@ -330,28 +330,28 @@ ResNeSt 与 RegNet 系列模型的精度、速度指标如下表所示，更多
 ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标如下表所示. 更多关于该系列模型的介绍可以参考： [ViT_and_DeiT 系列模型文档](../models/ViT_and_DeiT.md)。
 
 
-| 模型                  | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
-|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|
-| ViT_small_<br/>patch16_224 | 0.7769  | 0.9342   | -                | -                |   9.41   | 48.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) |
-| ViT_base_<br/>patch16_224 | 0.8195   | 0.9617   | -    | -                |  16.85   | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_224_infer.tar) |
-| ViT_base_<br/>patch16_384 | 0.8414  | 0.9717   |    -              |      -           |    49.35     | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_384_infer.tar) |
-| ViT_base_<br/>patch32_384 | 0.8176   | 0.9613   | - | - | 12.66 | 88.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch32_384_infer.tar) |
-| ViT_large_<br/>patch16_224 | 0.8323  | 0.9650   | - | - | 59.65 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_224_infer.tar) |
-|ViT_large_<br/>patch16_384| 0.8513 | 0.9736    | -                | -                | 174.70   | 304.12    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_384_infer.tar) |
-|ViT_large_<br/>patch32_384| 0.8153 | 0.9608    | -                | -                | 44.24    | 306.48    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch32_384_infer.tar) |
+| 模型                  | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
+|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------|
+| ViT_small_<br/>patch16_224 | 0.7769  | 0.9342   | 3.71             | 9.05             | 16.72             |   9.41   | 48.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) |
+| ViT_base_<br/>patch16_224 | 0.8195   | 0.9617   | 6.12             | 14.84            | 28.51             |  16.85   | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_224_infer.tar) |
+| ViT_base_<br/>patch16_384 | 0.8414  | 0.9717   | 14.15            | 48.38            | 95.06             |    49.35     | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_384_infer.tar) |
+| ViT_base_<br/>patch32_384 | 0.8176   | 0.9613   | 4.94             | 13.43            | 24.08             | 12.66 | 88.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch32_384_infer.tar) |
+| ViT_large_<br/>patch16_224 | 0.8323  | 0.9650   | 15.53            | 49.50            | 94.09             | 59.65 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_224_infer.tar) |
+|ViT_large_<br/>patch16_384| 0.8513 | 0.9736    | 39.51            | 152.46           | 304.06            | 174.70   | 304.12    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_384_infer.tar) |
+|ViT_large_<br/>patch32_384| 0.8153 | 0.9608    | 11.44            | 36.09            | 70.63             | 44.24    | 306.48    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch32_384_infer.tar) |
 
 
 
-| 模型                  | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
-|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|
-| DeiT_tiny_<br>patch16_224 | 0.718 | 0.910 | -                | -                |   1.07   | 5.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) |
-| DeiT_small_<br>patch16_224 | 0.796 | 0.949 | -    | -                |  4.24   | 21.97 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_patch16_224_infer.tar) |
-| DeiT_base_<br>patch16_224 | 0.817 | 0.957 |    -              |      -           |    16.85     | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_224_infer.tar) |
-| DeiT_base_<br>patch16_384 | 0.830 | 0.962 | - | - | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_384_infer.tar) |
-| DeiT_tiny_<br>distilled_patch16_224 | 0.741 | 0.918 | - | - | 1.08 | 5.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_distilled_patch16_224_infer.tar) |
-| DeiT_small_<br>distilled_patch16_224 | 0.809 | 0.953 | - | - | 4.26 | 22.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_distilled_patch16_224_infer.tar) |
-| DeiT_base_<br>distilled_patch16_224 | 0.831 | 0.964 | - | - | 16.93 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_224_infer.tar) |
-| DeiT_base_<br>distilled_patch16_384 | 0.851 | 0.973 | - | - | 49.43 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_384_infer.tar) |
+| 模型                  | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
+|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------|
+| DeiT_tiny_<br>patch16_224 | 0.718 | 0.910 | 3.61        | 3.94            | 6.10            |   1.07   | 5.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) |
+| DeiT_small_<br>patch16_224 | 0.796 | 0.949 | 3.61 | 6.24            | 10.49           |  4.24   | 21.97 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_patch16_224_infer.tar) |
+| DeiT_base_<br>patch16_224 | 0.817 | 0.957 | 6.13             | 14.87            |      28.50      |    16.85     | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_224_infer.tar) |
+| DeiT_base_<br>patch16_384 | 0.830 | 0.962 | 14.12            | 48.80            | 97.60 | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_384_infer.tar) |
+| DeiT_tiny_<br>distilled_patch16_224 | 0.741 | 0.918 | 3.51             | 4.05             | 6.03 | 1.08 | 5.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_distilled_patch16_224_infer.tar) |
+| DeiT_small_<br>distilled_patch16_224 | 0.809 | 0.953 | 3.70             | 6.20             | 10.53 | 4.26 | 22.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_distilled_patch16_224_infer.tar) |
+| DeiT_base_<br>distilled_patch16_224 | 0.831 | 0.964 | 6.17             | 14.94            | 28.58 | 16.93 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_224_infer.tar) |
+| DeiT_base_<br>distilled_patch16_384 | 0.851 | 0.973 | 14.12            | 48.76            | 97.09 | 49.43 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_384_infer.tar) |
 
 <a name="13"></a>
 
@@ -360,18 +360,18 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 关于 RepVGG 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RepVGG 系列模型文档](../models/RepVGG.md)。
 
 
-| 模型                     | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
-|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
-| RepVGG_A0   | 0.7131    | 0.9016    |  |  | 1.36 | 8.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A0_infer.tar) |
-| RepVGG_A1   | 0.7380    | 0.9146    |  |  | 2.37 | 12.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A1_infer.tar) |
-| RepVGG_A2   | 0.7571    | 0.9264    |  |  | 5.12 | 25.50 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A2_infer.tar) |
-| RepVGG_B0   | 0.7450    | 0.9213    |  |  | 3.06 | 14.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B0_infer.tar) |
-| RepVGG_B1   | 0.7773    | 0.9385    |  |  | 11.82 | 51.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1_infer.tar) |
-| RepVGG_B2   | 0.7813    | 0.9410    |  |  | 18.38 | 80.32 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2_infer.tar) |
-| RepVGG_B1g2 | 0.7732    | 0.9359    |  |  | 8.82 | 41.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g2_infer.tar) |
-| RepVGG_B1g4 | 0.7675    | 0.9335    |  |  | 7.31 | 36.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g4_infer.tar) |
-| RepVGG_B2g4 | 0.7881    | 0.9448    |  |  | 11.34 | 55.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2g4_infer.tar) |
-| RepVGG_B3g4 | 0.7965    | 0.9485    |  |  | 16.07 | 75.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B3g4_infer.tar) |
+| 模型                     | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
+|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
+| RepVGG_A0   | 0.7131    | 0.9016    |  |  |  | 1.36 | 8.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A0_infer.tar) |
+| RepVGG_A1   | 0.7380    | 0.9146    |  |  |  | 2.37 | 12.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A1_infer.tar) |
+| RepVGG_A2   | 0.7571    | 0.9264    |  |  |  | 5.12 | 25.50 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A2_infer.tar) |
+| RepVGG_B0   | 0.7450    | 0.9213    |  |  |  | 3.06 | 14.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B0_infer.tar) |
+| RepVGG_B1   | 0.7773    | 0.9385    |  |  |  | 11.82 | 51.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1_infer.tar) |
+| RepVGG_B2   | 0.7813    | 0.9410    |  |  |  | 18.38 | 80.32 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2_infer.tar) |
+| RepVGG_B1g2 | 0.7732    | 0.9359    |  |  |  | 8.82 | 41.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g2_infer.tar) |
+| RepVGG_B1g4 | 0.7675    | 0.9335    |  |  |  | 7.31 | 36.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g4_infer.tar) |
+| RepVGG_B2g4 | 0.7881    | 0.9448    |  |  |  | 11.34 | 55.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2g4_infer.tar) |
+| RepVGG_B3g4 | 0.7965    | 0.9485    |  |  |  | 16.07 | 75.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B3g4_infer.tar) |
 
 <a name="14"></a>
 
@@ -405,16 +405,16 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 
 关于 SwinTransformer 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[SwinTransformer 系列模型文档](../models/SwinTransformer.md)。
 
-| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
-| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| SwinTransformer_tiny_patch4_window7_224    | 0.8069 | 0.9534 |                  |                  | 4.35  | 28.26   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
-| SwinTransformer_small_patch4_window7_224   | 0.8275 | 0.9613 |                  |                  | 8.51  | 49.56   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
-| SwinTransformer_base_patch4_window7_224    | 0.8300 | 0.9626 |                  |                  | 15.13 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
-| SwinTransformer_base_patch4_window12_384   | 0.8439 | 0.9693 |                  |                  | 44.45 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
-| SwinTransformer_base_patch4_window7_224<sup>[1]</sup>     | 0.8487 | 0.9746 |                  |                  | 15.13 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
-| SwinTransformer_base_patch4_window12_384<sup>[1]</sup>    | 0.8642 | 0.9807 |                  |                  | 44.45 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
-| SwinTransformer_large_patch4_window7_224<sup>[1]</sup>    | 0.8596 | 0.9783 |                  |                  | 34.02 | 196.43  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_infer.tar) |
-| SwinTransformer_large_patch4_window12_384<sup>[1]</sup>   | 0.8719 | 0.9823 |                  |                  | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_infer.tar) |
+| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
+| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| SwinTransformer_tiny_patch4_window7_224    | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35  | 28.26   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
+| SwinTransformer_small_patch4_window7_224   | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51  | 49.56   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
+| SwinTransformer_base_patch4_window7_224    | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
+| SwinTransformer_base_patch4_window12_384   | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
+| SwinTransformer_base_patch4_window7_224<sup>[1]</sup>     | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
+| SwinTransformer_base_patch4_window12_384<sup>[1]</sup>    | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
+| SwinTransformer_large_patch4_window7_224<sup>[1]</sup>    | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_infer.tar) |
+| SwinTransformer_large_patch4_window12_384<sup>[1]</sup>   | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_infer.tar) |
 
 [1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
 
@@ -424,13 +424,13 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 
 关于 LeViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[LeViT 系列模型文档](../models/LeViT.md)。
 
-| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(M) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
-| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| LeViT_128S | 0.7598    | 0.9269    |                  |                  | 281    | 7.42     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/eViT_128S_infer.tar) |
-| LeViT_128 | 0.7810    | 0.9371    |                  |                  | 365    | 8.87     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
-| LeViT_192 | 0.7934    | 0.9446    |                  |                  | 597    | 10.61     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) |
-| LeViT_256 | 0.8085    | 0.9497    |                  |                  | 1049    | 18.45    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) |
-| LeViT_384 | 0.8191   | 0.9551    |                  |                  | 2234    | 38.45    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_384_infer.tar) |
+| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
+| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| LeViT_128S | 0.7598    | 0.9269    |                  |                  |                  | 281    | 7.42     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/eViT_128S_infer.tar) |
+| LeViT_128 | 0.7810    | 0.9371    |                  |                  |                  | 365    | 8.87     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
+| LeViT_192 | 0.7934    | 0.9446    |                  |                  |                  | 597    | 10.61     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) |
+| LeViT_256 | 0.8085    | 0.9497    |                  |                  |                  | 1049    | 18.45    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) |
+| LeViT_384 | 0.8191   | 0.9551    |                  |                  |                  | 2234    | 38.45    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_384_infer.tar) |
 
 **注**：与 Reference 的精度差异源于数据预处理不同及未使用蒸馏的 head 作为输出。
 
@@ -440,14 +440,14 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 
 关于 Twins 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[Twins 系列模型文档](../models/Twins.md)。
 
-| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
-| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| pcpvt_small | 0.8082    | 0.9552    |                  |                  |3.67    | 24.06    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_small_infer.tar) |
-| pcpvt_base | 0.8242    | 0.9619    |                  |                  | 6.44    | 43.83    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_base_infer.tar) |
-| pcpvt_large | 0.8273    | 0.9650    |                  |                  | 9.50    | 60.99     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_large_infer.tar) |
-| alt_gvt_small | 0.8140    | 0.9546    |                  |                  |2.81   | 24.06   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_small_infer.tar) |
-| alt_gvt_base | 0.8294   | 0.9621    |                  |                  | 8.34   | 56.07   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_base_infer.tar) |
-| alt_gvt_large | 0.8331   | 0.9642    |                  |                  | 14.81   | 99.27    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_large_infer.tar) |
+| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
+| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| pcpvt_small | 0.8082    | 0.9552    | 7.32 | 10.51 | 15.27 |3.67    | 24.06    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_small_infer.tar) |
+| pcpvt_base | 0.8242    | 0.9619    | 12.20 | 16.22 | 23.16 | 6.44    | 43.83    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_base_infer.tar) |
+| pcpvt_large | 0.8273    | 0.9650    | 16.47 | 22.90 | 32.73 | 9.50    | 60.99     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_large_infer.tar) |
+| alt_gvt_small | 0.8140    | 0.9546    | 6.94 | 9.01 | 12.27 |2.81   | 24.06   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_small_infer.tar) |
+| alt_gvt_base | 0.8294   | 0.9621    | 9.37 | 15.02 | 24.54 | 8.34   | 56.07   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_base_infer.tar) |
+| alt_gvt_large | 0.8331   | 0.9642    | 11.76 | 22.08 | 35.12 | 14.81   | 99.27    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_large_infer.tar) |
 
 **注**：与 Reference 的精度差异源于数据预处理不同。
 
diff --git a/docs/zh_CN/models/SwinTransformer.md b/docs/zh_CN/models/SwinTransformer.md
index 4e62f01d2..40a873274 100644
--- a/docs/zh_CN/models/SwinTransformer.md
+++ b/docs/zh_CN/models/SwinTransformer.md
@@ -4,6 +4,7 @@
 
 * [1. 概述](#1)
 * [2. 精度、FLOPS 和参数量](#2)
+* [3. 基于V100 GPU 的预测速度](#3)
 
 <a name='1'></a>
 
@@ -28,3 +29,20 @@ Swin Transformer 是一种新的视觉 Transformer 网络，可以用作计算
 [1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
 
 **注**：与 Reference 的精度差异源于数据预处理不同。
+
+<a name='3'></a>
+
+## 3. 基于 V100 GPU 的预测速度
+
+| 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) |
+| ------------------------------------------------------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
+| SwinTransformer_tiny_patch4_window7_224                 | 224       | 256               | 6.59                           | 9.68                           | 16.32                          |
+| SwinTransformer_small_patch4_window7_224                | 224       | 256               | 12.54                          | 17.07                          | 28.08                          |
+| SwinTransformer_base_patch4_window7_224                 | 224       | 256               | 13.37                          | 23.53                          | 39.11                          |
+| SwinTransformer_base_patch4_window12_384                | 384       | 384               | 19.52                          | 64.56                          | 123.30                         |
+| SwinTransformer_base_patch4_window7_224<sup>[1]</sup>   | 224       | 256               | 13.53                          | 23.46                          | 39.13                          |
+| SwinTransformer_base_patch4_window12_384<sup>[1]</sup>  | 384       | 384               | 19.65                          | 64.72                          | 123.42                         |
+| SwinTransformer_large_patch4_window7_224<sup>[1]</sup>  | 224       | 256               | 15.74                          | 38.57                          | 71.49                          |
+| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 384       | 384               | 32.61                          | 116.59                         | 223.23                         |
+
+[1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
diff --git a/docs/zh_CN/models/Twins.md b/docs/zh_CN/models/Twins.md
index f967b98f8..623ebf837 100644
--- a/docs/zh_CN/models/Twins.md
+++ b/docs/zh_CN/models/Twins.md
@@ -4,6 +4,7 @@
 
 * [1. 概述](#1)
 * [2. 精度、FLOPS 和参数量](#2)
+* [3. 基于V100 GPU 的预测速度](#3)
 
 <a name='1'></a>
 
@@ -24,3 +25,16 @@ Twins 网络包括 Twins-PCPVT 和 Twins-SVT，其重点对空间注意力机制
 | alt_gvt_large | 0.8331 | 0.9642 | 0.837 | - | 14.8 | 99.2   |
 
 **注**：与 Reference 的精度差异源于数据预处理不同。
+
+<a name='3'></a>
+
+## 3. 基于 V100 GPU 的预测速度
+
+| 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) |
+| ------------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
+| pcpvt_small   | 224       | 256               | 7.32                           | 10.51                          | 15.27                          |
+| pcpvt_base    | 224       | 256               | 12.20                          | 16.22                          | 23.16                          |
+| pcpvt_large   | 224       | 256               | 16.47                          | 22.90                          | 32.73                          |
+| alt_gvt_small | 224       | 256               | 6.94                           | 9.01                           | 12.27                          |
+| alt_gvt_base  | 224       | 256               | 9.37                           | 15.02                          | 24.54                          |
+| alt_gvt_large | 224       | 256               | 11.76                          | 22.08                          | 35.12                          |
diff --git a/docs/zh_CN/models/ViT_and_DeiT.md b/docs/zh_CN/models/ViT_and_DeiT.md
index 63b1363dd..fed45b932 100644
--- a/docs/zh_CN/models/ViT_and_DeiT.md
+++ b/docs/zh_CN/models/ViT_and_DeiT.md
@@ -4,6 +4,7 @@
 
 * [1. 概述](#1)
 * [2. 精度、FLOPS 和参数量](#2)
+* [3. 基于V100 GPU 的预测速度](#3)
 
 <a name='1'></a>
 
@@ -41,3 +42,29 @@ DeiT（Data-efficient Image Transformers）系列模型是由 FaceBook 在 2020
 | DeiT_base_distilled_patch16_384  | 0.851 | 0.973 | 0.852 | 0.972 |      |      |
 
 关于 Params、FLOPs、Inference speed 等信息，敬请期待。
+
+<a name='3'></a>
+
+## 3. 基于 V100 GPU 的预测速度
+
+| 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) |
+| -------------------------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
+| ViT_small_<br/>patch16_224 | 256       | 224               | 3.71                           | 9.05                           | 16.72                          |
+| ViT_base_<br/>patch16_224  | 256       | 224               | 6.12                           | 14.84                          | 28.51                          |
+| ViT_base_<br/>patch16_384  | 384       | 384               | 14.15                          | 48.38                          | 95.06                          |
+| ViT_base_<br/>patch32_384  | 384       | 384               | 4.94                           | 13.43                          | 24.08                          |
+| ViT_large_<br/>patch16_224 | 256       | 224               | 15.53                          | 49.50                          | 94.09                          |
+| ViT_large_<br/>patch16_384 | 384       | 384               | 39.51                          | 152.46                         | 304.06                         |
+| ViT_large_<br/>patch32_384 | 384       | 384               | 11.44                          | 36.09                          | 70.63                          |
+
+| 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) |
+| ------------------------------------ | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
+| DeiT_tiny_<br>patch16_224            | 256       | 224               | 3.61                           | 3.94                           | 6.10                           |
+| DeiT_small_<br>patch16_224           | 256       | 224               | 3.61                           | 6.24                           | 10.49                          |
+| DeiT_base_<br>patch16_224            | 256       | 224               | 6.13                           | 14.87                          | 28.50                          |
+| DeiT_base_<br>patch16_384            | 384       | 384               | 14.12                          | 48.80                          | 97.60                          |
+| DeiT_tiny_<br>distilled_patch16_224  | 256       | 224               | 3.51                           | 4.05                           | 6.03                           |
+| DeiT_small_<br>distilled_patch16_224 | 256       | 224               | 3.70                           | 6.20                           | 10.53                          |
+| DeiT_base_<br>distilled_patch16_224  | 256       | 224               | 6.17                           | 14.94                          | 28.58                          |
+| DeiT_base_<br>distilled_patch16_384  | 384       | 384               | 14.12                          | 48.76                          | 97.09                          |
+