add en docs and fix format

configs/ResNeSt/ResNeSt50.yaml

@@ -45,6 +45,7 @@ TRAIN:
         - RandFlipImage:
             flip_code: 1
         - AutoAugment:
+        - RandomErasing: 
         - NormalizeImage:
             scale: 1./255.
             mean: [0.485, 0.456, 0.406]

docs/en/models/ResNeSt_RegNet.md

+## Overview
+
+The ResNeSt series was proposed in 2020. The original resnet network structure has been improved by introducing K groups and adding an attention module similar to SEBlock in different groups, the accuracy is greater than that of the basic model ResNet, but the parameter amount and flops are almost the same as the basic ResNet.
+
+## Accuracy, FLOPs and Parameters
+
+| Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| ResNeSt50        | 0.8102 | 0.9542|  0.8113 |            -|5.39     | 27.5   |
\ No newline at end of file

ppcls/modeling/architectures/__init__.py

@@ -43,6 +43,7 @@ from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44
 from .darts_gs import DARTS_GS_6M, DARTS_GS_4M
 from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet
 from .ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
+from .resnest import ResNeSt50, ResNeSt101, ResNeSt200, ResNeSt269, ResNeSt50_fast_1s1x64d, ResNeSt50_fast_2s1x64d, ResNeSt50_fast_4s1x64d, ResNeSt50_fast_1s2x40d, ResNeSt50_fast_2s2x40d, ResNeSt50_fast_2s2x40d, ResNeSt50_fast_4s2x40d, ResNeSt50_fast_1s4x24d
 
 # distillation model
 from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd