update VehicleReID model

docs/en/application/vehicle_recognition_en.md

@@ -58,10 +58,7 @@ This method is used in VERI-Wild dataset. This dataset was captured in a large C
 |   GLAMOR(Resnet50+PGN)[3]    |   77.15   |   92.13   |   97.43   |
 |      PVEN(Resnet50)[4]       |   79.8    |   94.01   |   98.06   |
 |    SAVER(VAE+Resnet50)[5]    |   80.9    |   93.78   |   97.93   |
-|    PaddleClas  baseline1     |   65.6    |   92.37   |   97.23   |
-|    PaddleClas  baseline2     |   80.09   | **93.81** | **98.26** |
-
- Baseline1 is the released, and baseline2 will be released soon.
+|     PaddleClas  baseline     |   80.57   | **93.81** | **98.06** |
 
 ### 2.2 Vehicle Fine-grained Classification
 
@@ -79,7 +76,7 @@ The images in the dataset mainly come from the network and monitoring  data. The
 |      Fine-Tuning DARTS[7]       |   95.9%    |
 |       Resnet50 + COOC[8]        |   95.6%    |
 |             A3M[9]              |   95.4%    |
-| PaddleClas  baseline (ResNet50) | **97.36**% |
+| PaddleClas  baseline (ResNet50) | **97.37**% |
 
 ## 3 References
 

docs/en/tutorials/quick_start_recognition_en.md

@@ -41,7 +41,7 @@ The detection model with the recognition inference model for the 4 directions (L
 | Cartoon Face Recognition Model| Cartoon Face Scenario  | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) |
 | Vehicle Fine-Grained Classfication Model | Vehicle Scenario  |   [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) |
 | Product Recignition Model | Product Scenario  |  [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
-| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERI_Wild_v1.0_infer.tar) | - | - |
+| Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
 
 
 Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar).

docs/zh_CN/application/vehicle_recognition.md

@@ -57,10 +57,7 @@ ReID，也就是 Re-identification，其定义是利用算法，在图像库中
 |   GLAMOR(Resnet50+PGN)[3]    |   77.15   |   92.13   |   97.43   |
 |      PVEN(Resnet50)[4]       |   79.8    |   94.01   |   98.06   |
 |    SAVER(VAE+Resnet50)[5]    |   80.9    |   93.78   |   97.93   |
-|    PaddleClas  baseline1     |   65.6    |   92.37   |   97.23   |
-|    PaddleClas  baseline2     |   80.09   | **93.81** | **98.26** |
-
-注：baseline1 为目前的开源模型，baseline2即将开源
+|     PaddleClas  baseline     |   80.57   | **93.81** | **98.06** |
 
 ### 2.2 车辆细分类
 

docs/zh_CN/tutorials/quick_start_recognition.md

@@ -41,7 +41,7 @@
 | 动漫人物识别模型 | 动漫人物场景  | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) |
 | 车辆细分类模型 | 车辆场景  |  [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) |
 | 商品识别模型 | 商品场景  |  [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
-| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERI_Wild_v1.0_infer.tar) | - | - |
+| 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
 
 
 本章节demo数据下载地址如下: [数据下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar)。

ppcls/configs/Vehicle/ResNet50_ReID.yaml

@@ -52,11 +52,8 @@ Optimizer:
   name: Momentum
   momentum: 0.9
   lr:
-    name: MultiStepDecay
+    name: Cosine
     learning_rate: 0.01
-    milestones: [30, 60, 70, 80, 90, 100, 120, 140]
-    gamma: 0.5
-    verbose: False
     last_epoch: -1
   regularizer:
     name: 'L2'