README.md 38.6 KB
Newer Older
littletomatodonkey's avatar
littletomatodonkey 已提交
1
[简体中文](README_cn.md) | English
D
dyning 已提交
2

D
dyning 已提交
3
# PaddleClas
D
dyning 已提交
4

littletomatodonkey's avatar
littletomatodonkey 已提交
5
## Introduction
D
dyning 已提交
6

littletomatodonkey's avatar
littletomatodonkey 已提交
7
PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
D
dyning 已提交
8

littletomatodonkey's avatar
littletomatodonkey 已提交
9 10

**Recent update**
littletomatodonkey's avatar
littletomatodonkey 已提交
11
- 2020.10.10 Add cpp inference demo and improve FAQ tutorial.
C
cuicheng01 已提交
12 13 14
- 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%.
- 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%.
- 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%.
littletomatodonkey's avatar
littletomatodonkey 已提交
15 16
- 2020.06.17 Add English documents.
- 2020.06.12 Add support for training and evaluation on Windows or CPU.
littletomatodonkey's avatar
littletomatodonkey 已提交
17
- [more](./docs/en/update_history_en.md)
littletomatodonkey's avatar
littletomatodonkey 已提交
18 19


littletomatodonkey's avatar
littletomatodonkey 已提交
20
## Features
littletomatodonkey's avatar
littletomatodonkey 已提交
21

C
cuicheng01 已提交
22
- Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 24 series of classification network structures and training configurations, 122 models' pretrained weights and their evaluation metrics.
littletomatodonkey's avatar
littletomatodonkey 已提交
23

C
cuicheng01 已提交
24
- SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.
littletomatodonkey's avatar
littletomatodonkey 已提交
25

littletomatodonkey's avatar
littletomatodonkey 已提交
26
- Data augmentation: PaddleClas provides detailed introduction of 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, code reproduction and effect evaluation in a unified experimental environment.
littletomatodonkey's avatar
littletomatodonkey 已提交
27

littletomatodonkey's avatar
littletomatodonkey 已提交
28
- Pretrained model with 100,000 categories: Based on `ResNet50_vd` model, Baidu open sourced the `ResNet50_vd` pretrained model trained on a 100,000-category dataset. In some practical scenarios, the  accuracy based on the pretrained weights can be increased by up to 30%.
littletomatodonkey's avatar
littletomatodonkey 已提交
29

littletomatodonkey's avatar
littletomatodonkey 已提交
30
- A variety of training modes, including multi-machine training, mixed precision training, etc.
littletomatodonkey's avatar
littletomatodonkey 已提交
31

littletomatodonkey's avatar
littletomatodonkey 已提交
32
- A variety of inference and deployment solutions, including TensorRT inference, Paddle-Lite inference, model service deployment, model quantification, Paddle Hub, etc.
littletomatodonkey's avatar
littletomatodonkey 已提交
33

littletomatodonkey's avatar
littletomatodonkey 已提交
34
- Support Linux, Windows, macOS and other systems.
littletomatodonkey's avatar
littletomatodonkey 已提交
35 36


littletomatodonkey's avatar
littletomatodonkey 已提交
37
## Tutorials
littletomatodonkey's avatar
littletomatodonkey 已提交
38

littletomatodonkey's avatar
littletomatodonkey 已提交
39 40 41
- [Installation](./docs/en/tutorials/install_en.md)
- [Quick start PaddleClas in 30 minutes](./docs/en/tutorials/quick_start_en.md)
- [Model introduction and model zoo](./docs/en/models/models_intro_en.md)
littletomatodonkey's avatar
littletomatodonkey 已提交
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
    - [Model zoo overview](#Model_zoo_overview)
    - [ResNet and Vd series](#ResNet_and_Vd_series)
    - [Mobile series](#Mobile_series)
    - [SEResNeXt and Res2Net series](#SEResNeXt_and_Res2Net_series)
    - [DPN and DenseNet series](#DPN_and_DenseNet_series)
    - [HRNet series](#HRNet_series)
    - [Inception series](#Inception_series)
    - [EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series)
    - [ResNeSt and RegNet series](#ResNeSt_and_RegNet_series)
- Model training/evaluation
    - [Data preparation](./docs/en/tutorials/data_en.md)
    - [Model training and finetuning](./docs/en/tutorials/getting_started_en.md)
    - [Model evaluation](./docs/en/tutorials/getting_started_en.md)
- Model prediction/inference
    - [Prediction based on training engine](./docs/en/extension/paddle_inference_en.md)
    - [Python inference](./docs/en/extension/paddle_inference_en.md)
    - C++ inference (coming soon)
    - [Serving deployment](./docs/en/extension/paddle_serving_en.md)
    - Mobile (coming soon)
    - [Model Quantization and Compression](docs/en/extension/paddle_quantization_en.md)
- Advanced tutorials
    - [Knowledge distillation](./docs/en/advanced_tutorials/distillation/distillation_en.md)
    - [Data augmentation](./docs/en/advanced_tutorials/image_augmentation/ImageAugment_en.md)
- Applications
    - [Transfer learning](./docs/en/application/transfer_learning_en.md)
    - [Pretrained model with 100,000 categories](./docs/en/application/transfer_learning_en.md)
    - [Generic object detection](./docs/en/application/object_detection_en.md)
littletomatodonkey's avatar
littletomatodonkey 已提交
69
- FAQ
littletomatodonkey's avatar
littletomatodonkey 已提交
70
    - [General image classification problems](./docs/en/faq_en.md)
littletomatodonkey's avatar
littletomatodonkey 已提交
71 72 73 74 75
    - [PaddleClas FAQ](./docs/en/faq_en.md)
- [Competition support](./docs/en/competition_support_en.md)
- [License](#License)
- [Contribution](#Contribution)

littletomatodonkey's avatar
littletomatodonkey 已提交
76

littletomatodonkey's avatar
littletomatodonkey 已提交
77 78
<a name="Model_zoo_overview"></a>
### Model zoo overview
littletomatodonkey's avatar
littletomatodonkey 已提交
79

C
cuicheng01 已提交
80
Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The  evaluation environment is as follows.
littletomatodonkey's avatar
littletomatodonkey 已提交
81

littletomatodonkey's avatar
littletomatodonkey 已提交
82 83
* CPU evaluation environment is based on Snapdragon 855 (SD855).
* The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times).
littletomatodonkey's avatar
littletomatodonkey 已提交
84

D
dyning 已提交
85

littletomatodonkey's avatar
littletomatodonkey 已提交
86
Curves of accuracy to the inference time of common server-side models are shown as follows.
littletomatodonkey's avatar
littletomatodonkey 已提交
87

littletomatodonkey's avatar
littletomatodonkey 已提交
88
![](./docs/images/models/T4_benchmark/t4.fp32.bs4.main_fps_top1.png)
D
dyning 已提交
89

littletomatodonkey's avatar
littletomatodonkey 已提交
90

littletomatodonkey's avatar
littletomatodonkey 已提交
91
Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.
littletomatodonkey's avatar
littletomatodonkey 已提交
92

littletomatodonkey's avatar
littletomatodonkey 已提交
93
![](./docs/images/models/mobile_arm_storage.png)
D
dyning 已提交
94

littletomatodonkey's avatar
littletomatodonkey 已提交
95
![](./docs/images/models/mobile_arm_top1.png)
D
dyning 已提交
96 97


D
dyning 已提交
98

littletomatodonkey's avatar
littletomatodonkey 已提交
99 100
<a name="ResNet_and_Vd_series"></a>
### ResNet and Vd series
D
dyning 已提交
101

littletomatodonkey's avatar
littletomatodonkey 已提交
102 103 104
Accuracy and inference time metrics of ResNet and Vd series models are shown as follows. More detailed information can be refered to [ResNet and Vd series tutorial](./docs/en/models/ResNet_and_vd_en.md).

| Model                 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                         |
littletomatodonkey's avatar
littletomatodonkey 已提交
105
|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129
| ResNet18            | 0.7098    | 0.8992    | 1.45606               | 3.56305              | 3.66     | 11.69     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar)            |
| ResNet18_vd         | 0.7226    | 0.9080    | 1.54557               | 3.85363              | 4.14     | 11.71     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar)         |
| ResNet34            | 0.7457    | 0.9214    | 2.34957               | 5.89821              | 7.36     | 21.8      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar)            |
| ResNet34_vd         | 0.7598    | 0.9298    | 2.43427               | 6.22257              | 7.39     | 21.82     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_vd_pretrained.tar)         |
| ResNet34_vd_ssld         | 0.7972    | 0.9490    | 2.43427               | 6.22257              | 7.39     | 21.82     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_vd_ssld_pretrained.tar)         |
| ResNet50            | 0.7650    | 0.9300    | 3.47712               | 7.84421              | 8.19     | 25.56     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar)            |
| ResNet50_vc         | 0.7835    | 0.9403    | 3.52346               | 8.10725              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vc_pretrained.tar)         |
| ResNet50_vd         | 0.7912    | 0.9444    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar)         |
| ResNet50_vd_v2      | 0.7984    | 0.9493    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar)      |
| ResNet101           | 0.7756    | 0.9364    | 6.07125               | 13.40573             | 15.52    | 44.55     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar)           |
| ResNet101_vd        | 0.8017    | 0.9497    | 6.11704               | 13.76222             | 16.1     | 44.57     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar)        |
| ResNet152           | 0.7826    | 0.9396    | 8.50198               | 19.17073             | 23.05    | 60.19     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar)           |
| ResNet152_vd        | 0.8059    | 0.9530    | 8.54376               | 19.52157             | 23.53    | 60.21     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar)        |
| ResNet200_vd        | 0.8093    | 0.9533    | 10.80619              | 25.01731             | 30.53    | 74.74     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar)        |
| ResNet50_vd_<br>ssld    | 0.8239    | 0.9610    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar)    |
| ResNet50_vd_<br>ssld_v2 | 0.8300    | 0.9640    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_v2_pretrained.tar) |
| ResNet101_vd_<br>ssld   | 0.8373    | 0.9669    | 6.11704               | 13.76222             | 16.1     | 44.57     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_ssld_pretrained.tar)   |


<a name="Mobile_series"></a>
### Mobile series

Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](./docs/en/models/Mobile_en.md).

C
cuicheng01 已提交
130
| Model                              | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1 | Flops(G) | Params(M) | Model storage size(M) | Download Address                                                                                                      |
littletomatodonkey's avatar
littletomatodonkey 已提交
131
|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178
| MobileNetV1_<br>x0_25                | 0.5143    | 0.7546    | 3.21985                | 0.07     | 0.46      | 1.9     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_25_pretrained.tar)                |
| MobileNetV1_<br>x0_5                 | 0.6352    | 0.8473    | 9.579599               | 0.28     | 1.31      | 5.2     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_5_pretrained.tar)                 |
| MobileNetV1_<br>x0_75                | 0.6881    | 0.8823    | 19.436399              | 0.63     | 2.55      | 10      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_75_pretrained.tar)                |
| MobileNetV1                      | 0.7099    | 0.8968    | 32.523048              | 1.11     | 4.19      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar)                      |
| MobileNetV1_<br>ssld                 | 0.7789    | 0.9394    | 32.523048              | 1.11     | 4.19      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_ssld_pretrained.tar)                 |
| MobileNetV2_<br>x0_25                | 0.5321    | 0.7652    | 3.79925                | 0.05     | 1.5       | 6.1     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_25_pretrained.tar)                |
| MobileNetV2_<br>x0_5                 | 0.6503    | 0.8572    | 8.7021                 | 0.17     | 1.93      | 7.8     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_5_pretrained.tar)                 |
| MobileNetV2_<br>x0_75                | 0.6983    | 0.8901    | 15.531351              | 0.35     | 2.58      | 10      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_75_pretrained.tar)                |
| MobileNetV2                      | 0.7215    | 0.9065    | 23.317699              | 0.6      | 3.44      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar)                      |
| MobileNetV2_<br>x1_5                 | 0.7412    | 0.9167    | 45.623848              | 1.32     | 6.76      | 26      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x1_5_pretrained.tar)                 |
| MobileNetV2_<br>x2_0                 | 0.7523    | 0.9258    | 74.291649              | 2.32     | 11.13     | 43      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x2_0_pretrained.tar)                 |
| MobileNetV2_<br>ssld                 | 0.7674    | 0.9339    | 23.317699              | 0.6      | 3.44      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_ssld_pretrained.tar)                 |
| MobileNetV3_<br>large_x1_25          | 0.7641    | 0.9295    | 28.217701              | 0.714    | 7.44      | 29      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_25_pretrained.tar)          |
| MobileNetV3_<br>large_x1_0           | 0.7532    | 0.9231    | 19.30835               | 0.45     | 5.47      | 21      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar)           |
| MobileNetV3_<br>large_x0_75          | 0.7314    | 0.9108    | 13.5646                | 0.296    | 3.91      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_75_pretrained.tar)          |
| MobileNetV3_<br>large_x0_5           | 0.6924    | 0.8852    | 7.49315                | 0.138    | 2.67      | 11      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar)           |
| MobileNetV3_<br>large_x0_35          | 0.6432    | 0.8546    | 5.13695                | 0.077    | 2.1       | 8.6     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_35_pretrained.tar)          |
| MobileNetV3_<br>small_x1_25          | 0.7067    | 0.8951    | 9.2745                 | 0.195    | 3.62      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_25_pretrained.tar)          |
| MobileNetV3_<br>small_x1_0           | 0.6824    | 0.8806    | 6.5463                 | 0.123    | 2.94      | 12      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_pretrained.tar)           |
| MobileNetV3_<br>small_x0_75          | 0.6602    | 0.8633    | 5.28435                | 0.088    | 2.37      | 9.6     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_75_pretrained.tar)          |
| MobileNetV3_<br>small_x0_5           | 0.5921    | 0.8152    | 3.35165                | 0.043    | 1.9       | 7.8     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_5_pretrained.tar)           |
| MobileNetV3_<br>small_x0_35          | 0.5303    | 0.7637    | 2.6352                 | 0.026    | 1.66      | 6.9     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_35_pretrained.tar)          |
| MobileNetV3_<br>small_x0_35_ssld          | 0.5555    | 0.7771    | 2.6352                 | 0.026    | 1.66      | 6.9     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_35_ssld_pretrained.tar)          |
| MobileNetV3_<br>large_x1_0_ssld      | 0.7896    | 0.9448    | 19.30835               | 0.45     | 5.47      | 21      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar)      |
| MobileNetV3_large_<br>x1_0_ssld_int8 | 0.7605    |     -      | 14.395                 |    -     |      -     | 10      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_int8_pretrained.tar) |
| MobileNetV3_small_<br>x1_0_ssld      | 0.7129    | 0.9010    | 6.5463                 | 0.123    | 2.94      | 12      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar)      |
| ShuffleNetV2                     | 0.6880    | 0.8845    | 10.941                 | 0.28     | 2.26      | 9       | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar)                     |
| ShuffleNetV2_<br>x0_25               | 0.4990    | 0.7379    | 2.329                  | 0.03     | 0.6       | 2.7     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x0_25_pretrained.tar)               |
| ShuffleNetV2_<br>x0_33               | 0.5373    | 0.7705    | 2.64335                | 0.04     | 0.64      | 2.8     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x0_33_pretrained.tar)               |
| ShuffleNetV2_<br>x0_5                | 0.6032    | 0.8226    | 4.2613                 | 0.08     | 1.36      | 5.6     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x0_5_pretrained.tar)                |
| ShuffleNetV2_<br>x1_5                | 0.7163    | 0.9015    | 19.3522                | 0.58     | 3.47      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x1_5_pretrained.tar)                |
| ShuffleNetV2_<br>x2_0                | 0.7315    | 0.9120    | 34.770149              | 1.12     | 7.32      | 28      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x2_0_pretrained.tar)                |
| ShuffleNetV2_<br>swish               | 0.7003    | 0.8917    | 16.023151              | 0.29     | 2.26      | 9.1     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_swish_pretrained.tar)               |
| DARTS_GS_4M                      | 0.7523    | 0.9215    | 47.204948              | 1.04     | 4.77      | 21      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DARTS_GS_4M_pretrained.tar)                      |
| DARTS_GS_6M                      | 0.7603    | 0.9279    | 53.720802              | 1.22     | 5.69      | 24      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DARTS_GS_6M_pretrained.tar)                      |
| GhostNet_<br>x0_5                    | 0.6688    | 0.8695    | 5.7143                 | 0.082    | 2.6       | 10      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)               |
| GhostNet_<br>x1_0                    | 0.7402    | 0.9165    | 13.5587                | 0.294    | 5.2       | 20      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)               |
| GhostNet_<br>x1_3                    | 0.7579    | 0.9254    | 19.9825                | 0.44     | 7.3       | 29      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)               |


<a name="SEResNeXt_and_Res2Net_series"></a>
### SEResNeXt and Res2Net series

Accuracy and inference time metrics of SEResNeXt and Res2Net series models are shown as follows. More detailed information can be refered to [SEResNext and_Res2Net series tutorial](./docs/en/models/SEResNext_and_Res2Net_en.md).


| Model                 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                         |
littletomatodonkey's avatar
littletomatodonkey 已提交
179
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213
| Res2Net50_<br>26w_4s          | 0.7933    | 0.9457    | 4.47188               | 9.65722              | 8.52     | 25.7      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_26w_4s_pretrained.tar)          |
| Res2Net50_vd_<br>26w_4s       | 0.7975    | 0.9491    | 4.52712               | 9.93247              | 8.37     | 25.06     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_vd_26w_4s_pretrained.tar)       |
| Res2Net50_<br>14w_8s          | 0.7946    | 0.9470    | 5.4026                | 10.60273             | 9.01     | 25.72     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_14w_8s_pretrained.tar)          |
| Res2Net101_vd_<br>26w_4s      | 0.8064    | 0.9522    | 8.08729               | 17.31208             | 16.67    | 45.22     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net101_vd_26w_4s_pretrained.tar)      |
| Res2Net200_vd_<br>26w_4s      | 0.8121    | 0.9571    | 14.67806              | 32.35032             | 31.49    | 76.21     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_pretrained.tar)      |
| Res2Net200_vd_<br>26w_4s_ssld | 0.8513    | 0.9742    | 14.67806              | 32.35032             | 31.49    | 76.21     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_ssld_pretrained.tar) |
| ResNeXt50_<br>32x4d           | 0.7775    | 0.9382    | 7.56327               | 10.6134              | 8.02     | 23.64     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar)           |
| ResNeXt50_vd_<br>32x4d        | 0.7956    | 0.9462    | 7.62044               | 11.03385             | 8.5      | 23.66     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_32x4d_pretrained.tar)        |
| ResNeXt50_<br>64x4d           | 0.7843    | 0.9413    | 13.80962              | 18.4712              | 15.06    | 42.36     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar)           |
| ResNeXt50_vd_<br>64x4d        | 0.8012    | 0.9486    | 13.94449              | 18.88759             | 15.54    | 42.38     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_64x4d_pretrained.tar)        |
| ResNeXt101_<br>32x4d          | 0.7865    | 0.9419    | 16.21503              | 19.96568             | 15.01    | 41.54     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x4d_pretrained.tar)          |
| ResNeXt101_vd_<br>32x4d       | 0.8033    | 0.9512    | 16.28103              | 20.25611             | 15.49    | 41.56     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_32x4d_pretrained.tar)       |
| ResNeXt101_<br>64x4d          | 0.7835    | 0.9452    | 30.4788               | 36.29801             | 29.05    | 78.12     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar)          |
| ResNeXt101_vd_<br>64x4d       | 0.8078    | 0.9520    | 30.40456              | 36.77324             | 29.53    | 78.14     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar)       |
| ResNeXt152_<br>32x4d          | 0.7898    | 0.9433    | 24.86299              | 29.36764             | 22.01    | 56.28     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_32x4d_pretrained.tar)          |
| ResNeXt152_vd_<br>32x4d       | 0.8072    | 0.9520    | 25.03258              | 30.08987             | 22.49    | 56.3      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_vd_32x4d_pretrained.tar)       |
| ResNeXt152_<br>64x4d          | 0.7951    | 0.9471    | 46.7564               | 56.34108             | 43.03    | 107.57    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_64x4d_pretrained.tar)          |
| ResNeXt152_vd_<br>64x4d       | 0.8108    | 0.9534    | 47.18638              | 57.16257             | 43.52    | 107.59    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_vd_64x4d_pretrained.tar)       |
| SE_ResNet18_vd            | 0.7333    | 0.9138    | 1.7691                | 4.19877              | 4.14     | 11.8      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet18_vd_pretrained.tar)            |
| SE_ResNet34_vd            | 0.7651    | 0.9320    | 2.88559               | 7.03291              | 7.84     | 21.98     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet34_vd_pretrained.tar)            |
| SE_ResNet50_vd            | 0.7952    | 0.9475    | 4.28393               | 10.38846             | 8.67     | 28.09     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar)            |
| SE_ResNeXt50_<br>32x4d        | 0.7844    | 0.9396    | 8.74121               | 13.563               | 8.02     | 26.16     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar)        |
| SE_ResNeXt50_vd_<br>32x4d     | 0.8024    | 0.9489    | 9.17134               | 14.76192             | 10.76    | 26.28     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_vd_32x4d_pretrained.tar)     |
| SE_ResNeXt101_<br>32x4d       | 0.7912    | 0.9420    | 18.82604              | 25.31814             | 15.02    | 46.28     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar)       |
| SENet154_vd               | 0.8140    | 0.9548    | 53.79794              | 66.31684             | 45.83    | 114.29    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar)               |


<a name="DPN_and_DenseNet_series"></a>
### DPN and DenseNet series

Accuracy and inference time metrics of DPN and DenseNet series models are shown as follows. More detailed information can be refered to [DPN and DenseNet series tutorial](./docs/en/models/DPN_DenseNet_en.md).


| Model                 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                         |
littletomatodonkey's avatar
littletomatodonkey 已提交
214
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
215 216 217 218 219 220 221 222 223 224
| DenseNet121 | 0.7566    | 0.9258    | 4.40447               | 9.32623              | 5.69     | 7.98      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet121_pretrained.tar) |
| DenseNet161 | 0.7857    | 0.9414    | 10.39152              | 22.15555             | 15.49    | 28.68     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet161_pretrained.tar) |
| DenseNet169 | 0.7681    | 0.9331    | 6.43598               | 12.98832             | 6.74     | 14.15     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet169_pretrained.tar) |
| DenseNet201 | 0.7763    | 0.9366    | 8.20652               | 17.45838             | 8.61     | 20.01     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet201_pretrained.tar) |
| DenseNet264 | 0.7796    | 0.9385    | 12.14722              | 26.27707             | 11.54    | 33.37     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet264_pretrained.tar) |
| DPN68       | 0.7678    | 0.9343    | 11.64915              | 12.82807             | 4.03     | 10.78     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DPN68_pretrained.tar)       |
| DPN92       | 0.7985    | 0.9480    | 18.15746              | 23.87545             | 12.54    | 36.29     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DPN92_pretrained.tar)       |
| DPN98       | 0.8059    | 0.9510    | 21.18196              | 33.23925             | 22.22    | 58.46     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DPN98_pretrained.tar)       |
| DPN107      | 0.8089    | 0.9532    | 27.62046              | 52.65353             | 35.06    | 82.97     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DPN107_pretrained.tar)      |
| DPN131      | 0.8070    | 0.9514    | 28.33119              | 46.19439             | 30.51    | 75.36     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DPN131_pretrained.tar)      |
D
dyning 已提交
225

littletomatodonkey's avatar
littletomatodonkey 已提交
226 227
<a name="HRNet_series"></a>
### HRNet series
D
dyning 已提交
228

littletomatodonkey's avatar
littletomatodonkey 已提交
229
Accuracy and inference time metrics of HRNet series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](./docs/en/models/HRNet_en.md).
littletomatodonkey's avatar
littletomatodonkey 已提交
230

littletomatodonkey's avatar
littletomatodonkey 已提交
231

littletomatodonkey's avatar
littletomatodonkey 已提交
232
| Model         | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                 |
littletomatodonkey's avatar
littletomatodonkey 已提交
233
|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
234 235 236 237 238 239 240 241 242 243
| HRNet_W18_C | 0.7692    | 0.9339    | 7.40636          | 13.29752         | 4.14     | 21.29     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar) |
| HRNet_W18_C_ssld | 0.81162    | 0.95804    | 7.40636          | 13.29752         | 4.14     | 21.29     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_ssld_pretrained.tar) |
| HRNet_W30_C | 0.7804    | 0.9402    | 9.57594          | 17.35485         | 16.23    | 37.71     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W30_C_pretrained.tar) |
| HRNet_W32_C | 0.7828    | 0.9424    | 9.49807          | 17.72921         | 17.86    | 41.23     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W32_C_pretrained.tar) |
| HRNet_W40_C | 0.7877    | 0.9447    | 12.12202         | 25.68184         | 25.41    | 57.55     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W40_C_pretrained.tar) |
| HRNet_W44_C | 0.7900    | 0.9451    | 13.19858         | 32.25202         | 29.79    | 67.06     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W44_C_pretrained.tar) |
| HRNet_W48_C | 0.7895    | 0.9442    | 13.70761         | 34.43572         | 34.58    | 77.47     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W48_C_pretrained.tar) |
| HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W48_C_pretrained.tar) |
| HRNet_W64_C | 0.7930    | 0.9461    | 17.57527         | 47.9533          | 57.83    | 128.06    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W64_C_pretrained.tar) |

littletomatodonkey's avatar
littletomatodonkey 已提交
244

littletomatodonkey's avatar
littletomatodonkey 已提交
245 246
<a name="Inception_series"></a>
### Inception series
littletomatodonkey's avatar
littletomatodonkey 已提交
247

littletomatodonkey's avatar
littletomatodonkey 已提交
248
Accuracy and inference time metrics of Inception series models are shown as follows. More detailed information can be refered to [Inception series tutorial](./docs/en/models/Inception_en.md).
D
dyning 已提交
249

D
dyning 已提交
250

littletomatodonkey's avatar
littletomatodonkey 已提交
251
| Model                 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                         |
littletomatodonkey's avatar
littletomatodonkey 已提交
252
|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
253 254 255 256 257 258 259
| GoogLeNet          | 0.7070    | 0.8966    | 1.88038               | 4.48882              | 2.88     | 8.46      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GoogLeNet_pretrained.tar)          |
| Xception41         | 0.7930    | 0.9453    | 4.96939               | 17.01361             | 16.74    | 22.69     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_pretrained.tar)         |
| Xception41_deeplab | 0.7955    | 0.9438    | 5.33541               | 17.55938             | 18.16    | 26.73     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_deeplab_pretrained.tar) |
| Xception65         | 0.8100    | 0.9549    | 7.26158               | 25.88778             | 25.95    | 35.48     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Xception65_pretrained.tar)         |
| Xception65_deeplab | 0.8032    | 0.9449    | 7.60208               | 26.03699             | 27.37    | 39.52     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Xception65_deeplab_pretrained.tar) |
| Xception71         | 0.8111    | 0.9545    | 8.72457               | 31.55549             | 31.77    | 37.28     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Xception71_pretrained.tar)         |
| InceptionV4        | 0.8077    | 0.9526    | 12.99342              | 25.23416             | 24.57    | 42.68     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar)        |
D
dyning 已提交
260 261


littletomatodonkey's avatar
littletomatodonkey 已提交
262 263
<a name="EfficientNet_and_ResNeXt101_wsl_series"></a>
### EfficientNet and ResNeXt101_wsl series
D
dyning 已提交
264

littletomatodonkey's avatar
littletomatodonkey 已提交
265
Accuracy and inference time metrics of EfficientNet and ResNeXt101_wsl series models are shown as follows. More detailed information can be refered to [EfficientNet and ResNeXt101_wsl series tutorial](./docs/en/models/EfficientNet_and_ResNeXt101_wsl_en.md).
D
dyning 已提交
266 267


littletomatodonkey's avatar
littletomatodonkey 已提交
268
| Model                       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                               |
littletomatodonkey's avatar
littletomatodonkey 已提交
269
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
270 271 272 273 274 275 276 277 278 279 280 281 282 283
| ResNeXt101_<br>32x8d_wsl      | 0.8255    | 0.9674    | 18.52528         | 34.25319         | 29.14    | 78.44     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x8d_wsl_pretrained.tar)      |
| ResNeXt101_<br>32x16d_wsl     | 0.8424    | 0.9726    | 25.60395         | 71.88384         | 57.55    | 152.66    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x16d_wsl_pretrained.tar)     |
| ResNeXt101_<br>32x32d_wsl     | 0.8497    | 0.9759    | 54.87396         | 160.04337        | 115.17   | 303.11    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x32d_wsl_pretrained.tar)     |
| ResNeXt101_<br>32x48d_wsl     | 0.8537    | 0.9769    | 99.01698256      | 315.91261        | 173.58   | 456.2     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x48d_wsl_pretrained.tar)     |
| Fix_ResNeXt101_<br>32x48d_wsl | 0.8626    | 0.9797    | 160.0838242      | 595.99296        | 354.23   | 456.2     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/Fix_ResNeXt101_32x48d_wsl_pretrained.tar) |
| EfficientNetB0            | 0.7738    | 0.9331    | 3.442            | 6.11476          | 0.72     | 5.1       | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB0_pretrained.tar)            |
| EfficientNetB1            | 0.7915    | 0.9441    | 5.3322           | 9.41795          | 1.27     | 7.52      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB1_pretrained.tar)            |
| EfficientNetB2            | 0.7985    | 0.9474    | 6.29351          | 10.95702         | 1.85     | 8.81      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB2_pretrained.tar)            |
| EfficientNetB3            | 0.8115    | 0.9541    | 7.67749          | 16.53288         | 3.43     | 11.84     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB3_pretrained.tar)            |
| EfficientNetB4            | 0.8285    | 0.9623    | 12.15894         | 30.94567         | 8.29     | 18.76     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB4_pretrained.tar)            |
| EfficientNetB5            | 0.8362    | 0.9672    | 20.48571         | 61.60252         | 19.51    | 29.61     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB5_pretrained.tar)            |
| EfficientNetB6            | 0.8400    | 0.9688    | 32.62402         | -                | 36.27    | 42        | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB6_pretrained.tar)            |
| EfficientNetB7            | 0.8430    | 0.9689    | 53.93823         | -                | 72.35    | 64.92     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB7_pretrained.tar)            |
| EfficientNetB0_<br>small      | 0.7580    | 0.9258    | 2.3076           | 4.71886          | 0.72     | 4.65      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB0_small_pretrained.tar)      |
D
dyning 已提交
284

littletomatodonkey's avatar
littletomatodonkey 已提交
285

littletomatodonkey's avatar
littletomatodonkey 已提交
286 287
<a name="ResNeSt_and_RegNet_series"></a>
### ResNeSt and RegNet series
littletomatodonkey's avatar
littletomatodonkey 已提交
288

littletomatodonkey's avatar
littletomatodonkey 已提交
289
Accuracy and inference time metrics of ResNeSt and RegNet series models are shown as follows. More detailed information can be refered to [ResNeSt and RegNet series tutorial](./docs/en/models/ResNeSt_RegNet_en.md).
littletomatodonkey's avatar
littletomatodonkey 已提交
290 291


littletomatodonkey's avatar
littletomatodonkey 已提交
292
| Model                    | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                                 |
littletomatodonkey's avatar
littletomatodonkey 已提交
293
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
littletomatodonkey's avatar
littletomatodonkey 已提交
294 295 296 297 298 299 300
| ResNeSt50_<br>fast_1s1x64d | 0.8035    | 0.9528    | 3.45405                | 8.72680                | 8.68     | 26.3      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
| ResNeSt50              | 0.8102    | 0.9542    | 6.69042    | 8.01664                | 10.78    | 27.5      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams)              |
| RegNetX_4GF            | 0.785     | 0.9416    |    6.46478              |      11.19862           | 8        | 22.1      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams)            |


<a name="License"></a>
## License
littletomatodonkey's avatar
littletomatodonkey 已提交
301

littletomatodonkey's avatar
littletomatodonkey 已提交
302
PaddleClas is released under the <a href="https://github.com/PaddlePaddle/PaddleClas/blob/master/LICENSE">Apache 2.0 license</a>
littletomatodonkey's avatar
littletomatodonkey 已提交
303

D
dyning 已提交
304

littletomatodonkey's avatar
littletomatodonkey 已提交
305 306
<a name="Contribution"></a>
## Contribution
D
dyning 已提交
307

littletomatodonkey's avatar
littletomatodonkey 已提交
308
Contributions are highly welcomed and we would really appreciate your feedback!!
littletomatodonkey's avatar
littletomatodonkey 已提交
309

littletomatodonkey's avatar
littletomatodonkey 已提交
310 311
- Thank [nblib](https://github.com/nblib) to fix bug of RandErasing.
- Thank [chenpy228](https://github.com/chenpy228) to fix some typos PaddleClas.