add inference model link

docs/zh_CN/algorithm_introduction/ImageNet_models.md

@@ -173,7 +173,7 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关
 | MobileNetV3_<br>small_x0_75          | 0.6602    | 0.8633    | 4.50  | 2.96  | 2.19  | 46.02    | 2.38      | 9.6     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams)          | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_75_infer.tar) |
 | MobileNetV3_<br>small_x0_5           | 0.5921    | 0.8152    | 2.89 | 2.04 | 1.62  | 22.60    | 1.91       | 7.8     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams)           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_5_infer.tar) |
 | MobileNetV3_<br>small_x0_35          | 0.5303    | 0.7637    | 2.23  | 1.66    | 1.43   | 14.56    | 1.67      | 6.9     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams)          | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_infer.tar) |
-| MobileNetV3_<br>small_x0_35_ssld          | 0.5555    | 0.7771    | 2.23 | 1.66 | 1.43 | 14.56    | 1.67      | 6.9     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams)          |           |
+| MobileNetV3_<br>small_x0_35_ssld          | 0.5555    | 0.7771    | 2.23 | 1.66 | 1.43 | 14.56    | 1.67      | 6.9     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams)          | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_ssld_infer.tar) |
 | MobileNetV3_<br>large_x1_0_ssld      | 0.7896    | 0.9448    | 16.55                            | 10.09                             | 6.84                              | 229.66     | 5.50      | 21      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) |
 | MobileNetV3_small_<br>x1_0_ssld      | 0.7129    | 0.9010    | 5.63                             | 3.65                              | 2.60                              | 63.67    | 2.95      | 12      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) |
 | ShuffleNetV2                     | 0.6880    | 0.8845    | 9.72  | 5.97   | 4.13    | 148.86     | 2.29      | 9       | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams)                     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_0_infer.tar) |
@@ -317,7 +317,7 @@ EfficientNet 与 ResNeXt101_wsl 系列模型的精度、速度指标如下表所
 ResNeSt 与 RegNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNeSt 与 RegNet 系列模型文档](../models/ResNeSt_RegNet.md)。
 
 
-| 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span>                                      | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span>                          |
+| 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                          |
 |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
 | ResNeSt50_<br>fast_1s1x64d | 0.8035    | 0.9528    | 2.73             | 5.33             | 8.24           | 4.36     | 26.27      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_fast_1s1x64d_infer.tar) |
 | ResNeSt50              | 0.8083    | 0.9542    | 7.36             | 10.23            | 13.84          | 5.40    | 27.54      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams)              | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_infer.tar) |
@@ -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) | 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-|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) |
-|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) |
+| 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 | FLOPs(G) | Params(M) | 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
+| 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) |
 
 <a name="13"></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) | 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
+| 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) |
 
 [1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
 
@@ -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) | 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
-| 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) |
+| 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) |
 
 **注**：与 Reference 的精度差异源于数据预处理不同。
 
@@ -502,9 +502,9 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 
 关于 TNT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[TNT 系列模型文档](../models/TNT.md)。
 
-| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址                                                     |
-| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
-| TNT_small | 0.8121   |0.9563  |                  |                  | 4.83   |  23.68    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) |               |  
+| 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                                      |
+| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| TNT_small | 0.8121   |0.9563  |                  |                  | 4.83   |  23.68    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/TNT_small_infer.tar) |
 
 **注**：TNT 模型的数据预处理部分 `NormalizeImage` 中的 `mean` 与 `std` 均为 0.5。