docs: fix link

docs/en/algorithm_introduction/ImageNet_models_en.md

@@ -63,7 +63,7 @@ Accuracy and inference time of the prtrained models based on SSLD distillation a
 
 | Model                  | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
 |---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|
-| ResNet34_vd_ssld         | 0.797    | 0.760  | 0.037  | 2.00             | 3.28             | 5.84              | 3.93     | 21.84     | <span style="white-space:nowrap;">[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld.tar)&emsp;&emsp;</span> |
+| ResNet34_vd_ssld         | 0.797    | 0.760  | 0.037  | 2.00             | 3.28             | 5.84              | 3.93     | 21.84     | <span style="white-space:nowrap;">[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar)&emsp;&emsp;</span> |
 | ResNet50_vd_ssld | 0.830    | 0.792    | 0.039 | 2.60             | 4.86             | 7.63              | 4.35     | 25.63     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) |
 | ResNet101_vd_ssld   | 0.837    | 0.802    | 0.035 | 4.43             | 8.25             | 12.60     | 8.08     | 44.67     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams)   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) |
 | Res2Net50_vd_26w_4s_ssld | 0.831    | 0.798    | 0.033 | 3.59             | 6.35             | 9.50              | 4.28     | 25.76     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_ssld_infer.tar) |
@@ -408,8 +408,8 @@ The accuracy and speed indicators of SwinTransformer series models are shown in
 | SwinTransformer_base_patch4_window12_384   | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [Download link](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   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](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   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](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  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](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 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_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  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
+| SwinTransformer_large_patch4_window12_384<sup>[1]</sup>   | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
 
 [1]：It is pre-trained based on the ImageNet22k dataset, and then transferred and learned from the ImageNet1k dataset.
 
@@ -421,7 +421,7 @@ The accuracy and speed indicators of LeViT series models are shown in the follow
 
 | Model       | 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) | Pretrained Model Download Address                                               | Inference Model Download Address                                      |
 | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| LeViT_128S | 0.7598    | 0.9269    |                  |                  |                  | 281    | 7.42     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/eViT_128S_infer.tar) |
+| LeViT_128S | 0.7598    | 0.9269    |                  |                  |                  | 281    | 7.42     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128S_infer.tar) |
 | LeViT_128 | 0.7810    | 0.9371    |                  |                  |                  | 365    | 8.87     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
 | LeViT_192 | 0.7934    | 0.9446    |                  |                  |                  | 597    | 10.61     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) |
 | LeViT_256 | 0.8085    | 0.9497    |                  |                  |                  | 1049    | 18.45    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) |

docs/en/models/models_intro_en.md

@@ -39,42 +39,41 @@ python tools/infer/predict.py \
 ## Pretrained model list and download address
 - ResNet and ResNet_vd series
   - ResNet series<sup>[[1](#ref1)]</sup>([paper link](http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html))
-    - [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams)
-    - [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams)
-    - [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams)
-    - [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_pretrained.pdparams)
-    - [ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_pretrained.pdparams)
+    - [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams)
+    - [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams)
+    - [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams)
+    - [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams)
+    - [ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams)
   - ResNet_vc、ResNet_vd series<sup>[[2](#ref2)]</sup>([paper link](https://arxiv.org/abs/1812.01187))
     - [ResNet50_vc](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams)
-    - [ResNet18_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams)
-    - [ResNet34_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_pretrained.pdparams)
-    - [ResNet34_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_ssld_pretrained.pdparams)
-    - [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams)
-    - [ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_v2_pretrained.pdparams)
-    - [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_pretrained.pdparams)
-    - [ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_vd_pretrained.pdparams)
-    - [ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet200_vd_pretrained.pdparams)
-    - [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams)
+    - [ResNet18_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams)
+    - [ResNet34_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams)
+    - [ResNet34_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)
+    - [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams)
+    - [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams)
+    - [ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams)
+    - [ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams)
+    - [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams)
     - [ResNet50_vd_ssld_v2](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_v2_pretrained.pdparams)
     - [Fix_ResNet50_vd_ssld_v2](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNet50_vd_ssld_v2_pretrained.pdparams)
-    - [ResNet101_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_ssld_pretrained.pdparams)
+    - [ResNet101_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams)
 
 
 - Mobile and Embedded Vision Applications Network series
   - MobileNetV3 series<sup>[[3](#ref3)]</sup>([paper link](https://arxiv.org/abs/1905.02244))
-    - [MobileNetV3_large_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_35_pretrained.pdparams)
-    - [MobileNetV3_large_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams)
-    - [MobileNetV3_large_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_75_pretrained.pdparams)
-    - [MobileNetV3_large_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams)
-    - [MobileNetV3_large_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_25_pretrained.pdparams)
-    - [MobileNetV3_small_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams)
-    - [MobileNetV3_small_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams)
-    - [MobileNetV3_small_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams)
-    - [MobileNetV3_small_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_pretrained.pdparams)
-    - [MobileNetV3_small_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_25_pretrained.pdparams)
-    - [MobileNetV3_large_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)
+    - [MobileNetV3_large_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams)
+    - [MobileNetV3_large_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams)
+    - [MobileNetV3_large_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams)
+    - [MobileNetV3_large_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams)
+    - [MobileNetV3_large_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams)
+    - [MobileNetV3_small_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams)
+    - [MobileNetV3_small_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams)
+    - [MobileNetV3_small_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams)
+    - [MobileNetV3_small_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams)
+    - [MobileNetV3_small_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams)
+    - [MobileNetV3_large_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)
     - [MobileNetV3_large_x1_0_ssld_int8]()(coming soon)
-    - [MobileNetV3_small_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)
+    - [MobileNetV3_small_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)
   - MobileNetV2 series<sup>[[4](#ref4)]</sup>([paper link](https://arxiv.org/abs/1801.04381))
     - [MobileNetV2_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams)
     - [MobileNetV2_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams)
@@ -84,11 +83,11 @@ python tools/infer/predict.py \
     - [MobileNetV2_x2_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams)
     - [MobileNetV2_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams)
   - MobileNetV1 series<sup>[[5](#ref5)]</sup>([paper link](https://arxiv.org/abs/1704.04861))
-    - [MobileNetV1_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_25_pretrained.pdparams)
-    - [MobileNetV1_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_5_pretrained.pdparams)
-    - [MobileNetV1_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_75_pretrained.pdparams)
-    - [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams)
-    - [MobileNetV1_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams)
+    - [MobileNetV1_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams)
+    - [MobileNetV1_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams)
+    - [MobileNetV1_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams)
+    - [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams)
+    - [MobileNetV1_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams)
   - ShuffleNetV2 series<sup>[[6](#ref6)]</sup>([paper link](https://arxiv.org/abs/1807.11164))
     - [ShuffleNetV2_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams)
     - [ShuffleNetV2_x0_33](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams)
@@ -144,7 +143,7 @@ python tools/infer/predict.py \
   - GoogLeNet series<sup>[[10](#ref10)]</sup>([paper link](https://arxiv.org/pdf/1409.4842.pdf))
     - [GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams)
   - InceptionV3 series<sup>[[26](#ref26)]</sup>([paper link](https://arxiv.org/abs/1512.00567))
-    - [InceptionV3](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams)
+    - [InceptionV3](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams)
   - InceptionV4 series<sup>[[11](#ref11)]</sup>([paper link](https://arxiv.org/abs/1602.07261))
     - [InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams)
   - Xception series<sup>[[12](#ref12)]</sup>([paper link](http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html))
@@ -157,16 +156,16 @@ python tools/infer/predict.py \
 
 - HRNet series
   - HRNet series<sup>[[13](#ref13)]</sup>([paper link](https://arxiv.org/abs/1908.07919))
-    - [HRNet_W18_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_pretrained.pdparams)
-    - [HRNet_W18_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_ssld_pretrained.pdparams)
-    - [HRNet_W30_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W30_C_pretrained.pdparams)
-    - [HRNet_W32_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W32_C_pretrained.pdparams)
-    - [HRNet_W40_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams)
-    - [HRNet_W44_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams)
-    - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams)
-    - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams)
-    - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams)
-    - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams)
+    - [HRNet_W18_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams)
+    - [HRNet_W18_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams)
+    - [HRNet_W30_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams)
+    - [HRNet_W32_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams)
+    - [HRNet_W40_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams)
+    - [HRNet_W44_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams)
+    - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams)
+    - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams)
+    - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams)
+    - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams)
 
 
 - DPN and DenseNet series
@@ -218,11 +217,11 @@ python tools/infer/predict.py \
     - [SwinTransformer_small_patch4_window7_224](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams)
     - [SwinTransformer_base_patch4_window7_224](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams)
     - [SwinTransformer_base_patch4_window12_384](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams)
-    - [SwinTransformer_base_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22k_pretrained.pdparams)
+    - [SwinTransformer_base_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams)
     - [SwinTransformer_base_patch4_window7_224_22kto1k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams)
-    - [SwinTransformer_large_patch4_window12_384_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22k_pretrained.pdparams)
+    - [SwinTransformer_large_patch4_window12_384_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams)
     - [SwinTransformer_large_patch4_window12_384_22kto1k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams)
-    - [SwinTransformer_large_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22k_pretrained.pdparams)
+    - [SwinTransformer_large_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams)
     - [SwinTransformer_large_patch4_window7_224_22kto1k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams)
 
 
@@ -234,10 +233,10 @@ python tools/infer/predict.py \
     - [SqueezeNet1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams)
     - [SqueezeNet1_1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams)
   - VGG series<sup>[[20](#ref20)]</sup>([paper link](https://arxiv.org/abs/1409.1556))
-    - [VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams)
-    - [VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams)
-    - [VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams)
-    - [VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams)
+    - [VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams)
+    - [VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams)
+    - [VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams)
+    - [VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams)
   - DarkNet series<sup>[[21](#ref21)]</sup>([paper link](https://arxiv.org/abs/1506.02640))
     - [DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams)
 

docs/zh_CN/models/models_intro.md

@@ -32,24 +32,23 @@
 
 - ResNet 及其 Vd 系列
   - ResNet 系列<sup>[[1](#ref1)]</sup>([论文地址](http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html))
-    - [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams)
-    - [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams)
-    - [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams)
-    - [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_pretrained.pdparams)
-    - [ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_pretrained.pdparams)
+    - [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams)
+    - [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams)
+    - [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams)
+    - [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams)
+    - [ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams)
   - ResNet_vc、ResNet_vd 系列<sup>[[2](#ref2)]</sup>([论文地址](https://arxiv.org/abs/1812.01187))
     - [ResNet50_vc](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams)
-    - [ResNet18_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams)
-    - [ResNet34_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_pretrained.pdparams)
-    - [ResNet34_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_ssld_pretrained.pdparams)
-    - [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams)
-    - [ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_v2_pretrained.pdparams)
-    - [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_pretrained.pdparams)
-    - [ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_vd_pretrained.pdparams)
-    - [ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet200_vd_pretrained.pdparams)
-    - [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams)
+    - [ResNet18_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams)
+    - [ResNet34_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams)
+    - [ResNet34_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)
+    - [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams)
+    - [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams)
+    - [ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams)
+    - [ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams)
+    - [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams)
     - [Fix_ResNet50_vd_ssld_v2](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNet50_vd_ssld_v2_pretrained.pdparams)
-    - [ResNet101_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_ssld_pretrained.pdparams)
+    - [ResNet101_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams)
 
 
 - 轻量级模型系列
@@ -66,19 +65,19 @@
     - [PPLCNet_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams)
     - [PPLCNet_x2_5_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams)
   - MobileNetV3 系列<sup>[[3](#ref3)]</sup>([论文地址](https://arxiv.org/abs/1905.02244))
-    - [MobileNetV3_large_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_35_pretrained.pdparams)
-    - [MobileNetV3_large_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams)
-    - [MobileNetV3_large_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_75_pretrained.pdparams)
-    - [MobileNetV3_large_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams)
-    - [MobileNetV3_large_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_25_pretrained.pdparams)
-    - [MobileNetV3_small_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams)
-    - [MobileNetV3_small_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams)
-    - [MobileNetV3_small_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams)
-    - [MobileNetV3_small_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_pretrained.pdparams)
-    - [MobileNetV3_small_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_25_pretrained.pdparams)
-    - [MobileNetV3_large_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)
+    - [MobileNetV3_large_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams)
+    - [MobileNetV3_large_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams)
+    - [MobileNetV3_large_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams)
+    - [MobileNetV3_large_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams)
+    - [MobileNetV3_large_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams)
+    - [MobileNetV3_small_x0_35](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams)
+    - [MobileNetV3_small_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams)
+    - [MobileNetV3_small_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams)
+    - [MobileNetV3_small_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams)
+    - [MobileNetV3_small_x1_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams)
+    - [MobileNetV3_large_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)
     - [MobileNetV3_large_x1_0_ssld_int8]()(coming soon)
-    - [MobileNetV3_small_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)
+    - [MobileNetV3_small_x1_0_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)
   - MobileNetV2 系列<sup>[[4](#ref4)]</sup>([论文地址](https://arxiv.org/abs/1801.04381))
     - [MobileNetV2_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams)
     - [MobileNetV2_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams)
@@ -88,11 +87,11 @@
     - [MobileNetV2_x2_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams)
     - [MobileNetV2_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams)
   - MobileNetV1 系列<sup>[[5](#ref5)]</sup>([论文地址](https://arxiv.org/abs/1704.04861))
-    - [MobileNetV1_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_25_pretrained.pdparams)
-    - [MobileNetV1_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_5_pretrained.pdparams)
-    - [MobileNetV1_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_75_pretrained.pdparams)
-    - [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams)
-    - [MobileNetV1_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams)
+    - [MobileNetV1_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams)
+    - [MobileNetV1_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams)
+    - [MobileNetV1_x0_75](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams)
+    - [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams)
+    - [MobileNetV1_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams)
   - ShuffleNetV2 系列<sup>[[6](#ref6)]</sup>([论文地址](https://arxiv.org/abs/1807.11164))
     - [ShuffleNetV2_x0_25](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams)
     - [ShuffleNetV2_x0_33](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams)
@@ -158,7 +157,7 @@
   - GoogLeNet 系列<sup>[[10](#ref10)]</sup>([论文地址](https://arxiv.org/pdf/1409.4842.pdf))
     - [GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams)
   - InceptionV3 系列<sup>[[26](#ref26)]</sup>([论文地址](https://arxiv.org/abs/1512.00567))
-    - [InceptionV3](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams)
+    - [InceptionV3](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams)
   - InceptionV4 系列<sup>[[11](#ref11)]</sup>([论文地址](https://arxiv.org/abs/1602.07261))
     - [InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams)
   - Xception 系列<sup>[[12](#ref12)]</sup>([论文地址](http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html))
@@ -171,16 +170,16 @@
 
 - HRNet 系列
   - HRNet 系列<sup>[[13](#ref13)]</sup>([论文地址](https://arxiv.org/abs/1908.07919))
-    - [HRNet_W18_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_pretrained.pdparams)
-    - [HRNet_W18_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_ssld_pretrained.pdparams)
-    - [HRNet_W30_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W30_C_pretrained.pdparams)
-    - [HRNet_W32_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W32_C_pretrained.pdparams)
-    - [HRNet_W40_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams)
-    - [HRNet_W44_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams)
-    - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams)
-    - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams)
-    - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams)
-    - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams)
+    - [HRNet_W18_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams)
+    - [HRNet_W18_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams)
+    - [HRNet_W30_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams)
+    - [HRNet_W32_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams)
+    - [HRNet_W40_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams)
+    - [HRNet_W44_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams)
+    - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams)
+    - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams)
+    - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams)
+    - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams)
 
 - DPN 与 DenseNet 系列
   - DPN 系列<sup>[[14](#ref14)]</sup>([论文地址](https://arxiv.org/abs/1707.01629))
@@ -228,11 +227,11 @@
     - [SwinTransformer_small_patch4_window7_224](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams)
     - [SwinTransformer_base_patch4_window7_224](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams)
     - [SwinTransformer_base_patch4_window12_384](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams)
-    - [SwinTransformer_base_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22k_pretrained.pdparams)
+    - [SwinTransformer_base_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams)
     - [SwinTransformer_base_patch4_window7_224_22kto1k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams)
-    - [SwinTransformer_large_patch4_window12_384_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22k_pretrained.pdparams)
+    - [SwinTransformer_large_patch4_window12_384_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams)
     - [SwinTransformer_large_patch4_window12_384_22kto1k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams)
-    - [SwinTransformer_large_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22k_pretrained.pdparams)
+    - [SwinTransformer_large_patch4_window7_224_22k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams)
     - [SwinTransformer_large_patch4_window7_224_22kto1k](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams)
   - ViT 系列<sup>[[31](#ref31)]</sup>([论文地址](https://arxiv.org/pdf/2010.11929.pdf))
       - [ViT_small_patch16_224](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams)
@@ -274,10 +273,10 @@
     - [SqueezeNet1_0](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams)
     - [SqueezeNet1_1](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams)
   - VGG 系列<sup>[[20](#ref20)]</sup>([论文地址](https://arxiv.org/abs/1409.1556))
-    - [VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams)
-    - [VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams)
-    - [VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams)
-    - [VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams)
+    - [VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams)
+    - [VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams)
+    - [VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams)
+    - [VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams)
   - DarkNet 系列<sup>[[21](#ref21)]</sup>([论文地址](https://arxiv.org/abs/1506.02640))
     - [DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams)
   - RepVGG 系列<sup>[[36](#ref36)]</sup>([论文地址](https://arxiv.org/pdf/2101.03697.pdf))