Merge pull request #1022 from cuicheng01/develop

Update ImageNet_models_en.md

README_ch.md

@@ -10,7 +10,7 @@
 
 - 2021.06.29 添加Swin-transformer系列模型，ImageNet1k数据集上Top1 acc最高精度可达87.2%；支持训练预测评估与whl包部署，预训练模型可以从[这里](docs/zh_CN/models/models_intro.md)下载。
 - 2021.06.22,23,24 PaddleClas官方研发团队带来技术深入解读三日直播课。课程回放：[https://aistudio.baidu.com/aistudio/course/introduce/24519](https://aistudio.baidu.com/aistudio/course/introduce/24519)
-- 2021.06.16 PaddleClas v2.2版本升级，集成Metric learning，向量检索等组件。新增商品识别、动漫人物识别、车辆识别和logo识别等4个图像识别应用。新增LeViT、TNT、DLA、HarDNet、RedNet系列24个预训练模型。
+- 2021.06.16 PaddleClas v2.2版本升级，集成Metric learning，向量检索等组件。新增商品识别、动漫人物识别、车辆识别和logo识别等4个图像识别应用。新增LeViT、Twins、TNT、DLA、HarDNet、RedNet系列30个预训练模型。
 - [more](./docs/zh_CN/update_history.md)
 
 ## 特性
@@ -18,7 +18,7 @@
 - 实用的图像识别系统：集成了目标检测、特征学习、图像检索等模块，广泛适用于各类图像识别任务。
 提供商品识别、车辆识别、logo识别和动漫人物识别等4个场景应用示例。
 
-- 丰富的预训练模型库：提供了33个系列共150个ImageNet预训练模型，其中6个精选系列模型支持结构快速修改。
+- 丰富的预训练模型库：提供了35个系列共164个ImageNet预训练模型，其中6个精选系列模型支持结构快速修改。
 
 - 全面易用的特征学习组件：集成arcmargin, triplet loss等12度量学习方法，通过配置文件即可随意组合切换。
 

README_en.md

@@ -9,7 +9,7 @@ PaddleClas is an image recognition toolset for industry and academia, helping us
 **Recent updates**
 
 - 2021.06.29 Add Swin-transformer series model，Highest top1 acc on ImageNet1k dataset reaches 87.2%, training, evaluation and inference are all supported. Pretrained models can be downloaded [here](docs/en/models/models_intro_en.md).
-- 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 24 pretrained models of LeViT, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
+- 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
 - [more](./docs/en/update_history_en.md)
 
 ## Features
@@ -17,7 +17,7 @@ PaddleClas is an image recognition toolset for industry and academia, helping us
 - A practical image recognition system consist of detection, feature learning and retrieval modules, widely applicable to all types of image recognition tasks.
 Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition and animation character recognition.
 
-- Rich library of pre-trained models: Provide a total of 150 ImageNet pre-trained models in 33 series, among which 6 selected series of models support fast structural modification.
+- Rich library of pre-trained models: Provide a total of 164 ImageNet pre-trained models in 35 series, among which 6 selected series of models support fast structural modification.
 
 - Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be combined and switched at will through configuration files.
 
@@ -51,7 +51,7 @@ Quick experience of image recognition：[Link](./docs/en/tutorials/quick_start_r
 - [Introduction to Image Recognition Systems](#Introduction_to_Image_Recognition_Systems)
 - [Demo images](#Demo_images)
 - Algorithms Introduction
-    - [Backbone Network and Pre-trained Model Library](./docs/en/ImageNet_models.md)
+    - [Backbone Network and Pre-trained Model Library](./docs/en/ImageNet_models_en.md)
     - [Mainbody Detection](./docs/en/application/mainbody_detection_en.md)
     - [Image Classification](./docs/en/tutorials/image_classification_en.md)
     - [Feature Learning](./docs/en/application/feature_learning_en.md)

docs/en/models/LeViT_en.md

+# LeViT series
+
+## Overview
+LeViT is a fast inference hybrid neural network for image classification tasks. Its design considers the performance of the network model on different hardware platforms, so it can better reflect the real scenarios of common applications. Through a large number of experiments, the author found a better way to combine the convolutional neural network and the Transformer system, and proposed an attention-based method to integrate the position information encoding in the Transformer. [Paper](https://arxiv.org/abs/2104.01136)。
+
+## Accuracy, FLOPS and Parameters
+
+| Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(M) | Params<br>(M) |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| LeViT-128S | 0.7598 | 0.9269 | 0.766 | 0.929 | 305  | 7.8 |
+| LeViT-128  | 0.7810 | 0.9371 | 0.786 | 0.940 | 406  | 9.2 |
+| LeViT-192  | 0.7934 | 0.9446 | 0.800 | 0.947 | 658  | 11 |
+| LeViT-256  | 0.8085 | 0.9497 | 0.816 | 0.954 | 1120 | 19 |
+| LeViT-384  | 0.8191 | 0.9551 | 0.826 | 0.960 | 2353 | 39 |
+
+
+**Note**：The difference in accuracy from Reference is due to the difference in data preprocessing and the absence of distilled head as output.

docs/en/models/Twins.md

+# Twins
+
+## Overview
+The Twins network includes Twins-PCPVT and Twins-SVT, which focuses on the meticulous design of the spatial attention mechanism, resulting in a simple but more effective solution. Since the architecture only involves matrix multiplication, and the current deep learning framework has a high degree of optimization for matrix multiplication, the architecture is very efficient and easy to implement. Moreover, this architecture can achieve excellent performance in a variety of downstream vision tasks such as image classification, target detection, and semantic segmentation. [Paper](https://arxiv.org/abs/2104.13840).
+
+## Accuracy, FLOPS and Parameters
+
+| Models        | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Params<br>(M) |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| pcpvt_small   | 0.8082 | 0.9552 | 0.812 | - | 3.7 | 24.1   |
+| pcpvt_base    | 0.8242 | 0.9619 | 0.827 | - | 6.4 | 43.8   |
+| pcpvt_large   | 0.8273 | 0.9650 | 0.831 | - | 9.5 | 60.9   |
+| alt_gvt_small | 0.8140 | 0.9546 | 0.817 | - | 2.8  | 24   |
+| alt_gvt_base  | 0.8294 | 0.9621 | 0.832 | - | 8.3  | 56   |
+| alt_gvt_large | 0.8331 | 0.9642 | 0.837 | - | 14.8 | 99.2   |
+
+**Note**:The difference in accuracy from Reference is due to the difference in data preprocessing.