Skip to content

P
PaddleClas

PaddlePaddle / PaddleClas
大约 1 年 前同步成功

通知 115
Star 4999
Fork 1114
P
PaddleClas

体验新版 GitCode，发现更多精彩内容 >>

切换分支/标签
查找文件 普通视图历史永久链接Permalink
README_en.md 59.5 KB
Newer Older
W
update README  
weishengyu 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 
[简体中文](README.md) | English

# PaddleClas

## Introduction

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.

**Recent update**

- 2021.06.16 PaddleClas release/2.2.
   - Add metric learning and vector search module.
   - Add product recognition, cartoon character recognition, car 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.

- 2021.05.14
   - Add `SwinTransformer` series pretrained models, whose Top-1 Acc on ImageNet-1k dataset reaches 87.19%.

- 2021.04.15
   - Add `MixNet` and `ReXNet` pretrained models, `MixNet_L`'s Top-1 Acc on ImageNet-1k reaches 78.6% and `ReXNet_3_0` reaches 82.09%.

- [more](./docs/en/update_history_en.md)


## Features

- Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 29 series of classification network structures and training configurations, 134 models' pretrained weights and their evaluation metrics.

- SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.

- 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.

- 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%.

- A variety of training modes, including multi-machine training, mixed precision training, etc.

- A variety of inference and deployment solutions, including TensorRT inference, Paddle-Lite inference, model service deployment, model quantification, Paddle Hub, etc.

- Support Linux, Windows, macOS and other systems.


## Community

* Scan the QR code below with your Wechat and send the message `分类` out, then you will be invited into the official technical exchange group.

<div align="center">
<img src="./docs/images/wx_group.jpeg"  width = "200" height = "200" />
</div>




## Tutorials

- [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)
    - [Model zoo overview](#Model_zoo_overview)
    - [SSLD pretrained models](#SSLD_pretrained_series)
    - [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)
    - [ViT and DeiT series](#ViT_and_DeiT)
    - [RepVGG series](#RepVGG)
    - [MixNet series](#MixNet)
    - [ReXNet series](#ReXNet)
    - [SwinTransformer series](#SwinTransformer)
    - [Others](#Others)
    - HS-ResNet: arxiv link: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf). Code and models are coming soon!
- 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)
    - [Configuration details](./docs/en/tutorials/config_en.md)
- Model prediction/inference
    - [Prediction based on training engine](./docs/en/tutorials/getting_started_en.md)
    - [Python inference](./docs/en/tutorials/getting_started_en.md)
    - [C++ inference](./deploy/cpp_infer/readme_en.md)
    - [Serving deployment](./deploy/hubserving/readme_en.md)
    - [Mobile](./deploy/lite/readme_en.md)
    - [Inference using whl ](./docs/en/whl_en.md)
    - [Model Quantization and Compression](deploy/slim/quant/README_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)
    - [Multilabel classification](./docs/en/advanced_tutorials/multilabel/multilabel_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)
- FAQ
    - [General image classification problems](./docs/en/faq_en.md)
    - [PaddleClas FAQ](./docs/en/faq_en.md)
- [Competition support](./docs/en/competition_support_en.md)
- [License](#License)
- [Contribution](#Contribution)


<a name="Model_zoo_overview"></a>
### Model zoo overview

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.

* 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).


Curves of accuracy to the inference time of common server-side models are shown as follows.

![](./docs/images/models/T4_benchmark/t4.fp32.bs1.main_fps_top1.png)


Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.

![](./docs/images/models/mobile_arm_storage.png)

![](./docs/images/models/mobile_arm_top1.png)

<a name="SSLD_pretrained_series"></a>
### SSLD pretrained models
Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to [SSLD distillation tutorial](./docs/en/advanced_tutorials/distillation/distillation_en.md).

* Server-side distillation pretrained models

| Model                  | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                         |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
| ResNet34_vd_ssld         | 0.797    | 0.760  | 0.037  | 2.434               | 6.222              | 7.39     | 21.82     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_ssld_pretrained.pdparams)         |
| ResNet50_vd_<br>ssld    | 0.824    | 0.791    | 0.033 |  3.531               | 8.090              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams)    |
| ResNet50_vd_<br>ssld_v2 | 0.830    | 0.792    | 0.039 | 3.531               | 8.090              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_v2_pretrained.pdparams) |
| ResNet101_vd_<br>ssld   | 0.837    | 0.802    | 0.035 |  6.117               | 13.762             | 16.1     | 44.57     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_ssld_pretrained.pdparams)   |
| Res2Net50_vd_<br>26w_4s_ssld | 0.831    | 0.798    | 0.033 |  4.527              | 9.657             | 8.37     | 25.06     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) |
| Res2Net101_vd_<br>26w_4s_ssld | 0.839    | 0.806    | 0.033 | 8.087              | 17.312             | 16.67    | 45.22     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.851    | 0.812    | 0.049 | 14.678              | 32.350             | 31.49    | 76.21     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
| HRNet_W18_C_ssld | 0.812    | 0.769   | 0.043 | 7.406          | 13.297         | 4.14     | 21.29     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_ssld_pretrained.pdparams) |
| HRNet_W48_C_ssld | 0.836    | 0.790   | 0.046  | 13.707         | 34.435         | 34.58    | 77.47     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.848    |  -    |  - |  31.697      |     94.995      | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |


* Mobile-side distillation pretrained models

| Model                  | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | SD855 time(ms)<br>bs=1 | Flops(G) | Params(M) | Model size(M) | Download Address   |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
| MobileNetV1_<br>ssld   | 0.779    | 0.710    | 0.069 |  32.523              | 1.11     | 4.19      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams)                 |
| MobileNetV2_<br>ssld                 | 0.767    | 0.722  | 0.045  | 23.318              | 0.6      | 3.44      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams)                 |
| MobileNetV3_<br>small_x0_35_ssld          | 0.556    | 0.530 | 0.026   | 2.635                 | 0.026    | 1.66      | 6.9     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_ssld_pretrained.pdparams)          |
| MobileNetV3_<br>large_x1_0_ssld      | 0.790    | 0.753  | 0.036  | 19.308           | 0.45     | 5.47      | 21      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)      |
| MobileNetV3_small_<br>x1_0_ssld      | 0.713    | 0.682  |  0.031  | 6.546                 | 0.123    | 2.94      | 12      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)      |
| GhostNet_<br>x1_3_ssld                    | 0.794    | 0.757   | 0.037 | 19.983                | 0.44     | 7.3       | 29      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams)               |


* Note: `Reference Top-1 Acc` means accuracy of pretrained models which are trained on ImageNet1k dataset.


<a name="ResNet_and_Vd_series"></a>
### ResNet and Vd series

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                                                                                         |
|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|
| ResNet18            | 0.7098    | 0.8992    | 1.45606               | 3.56305              | 3.66     | 11.69     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams)            |
| ResNet18_vd         | 0.7226    | 0.9080    | 1.54557               | 3.85363              | 4.14     | 11.71     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams)         |
| ResNet34            | 0.7457    | 0.9214    | 2.34957               | 5.89821              | 7.36     | 21.8      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams)            |
| ResNet34_vd         | 0.7598    | 0.9298    | 2.43427               | 6.22257              | 7.39     | 21.82     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_pretrained.pdparams)         |
| 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/dygraph/ResNet34_vd_ssld_pretrained.pdparams)         |
| ResNet50            | 0.7650    | 0.9300    | 3.47712               | 7.84421              | 8.19     | 25.56     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams)            |
| ResNet50_vc         | 0.7835    | 0.9403    | 3.52346               | 8.10725              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams)         |
| ResNet50_vd         | 0.7912    | 0.9444    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams)         |
| 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/dygraph/ResNet50_vd_v2_pretrained.pdparams)      |
| ResNet101           | 0.7756    | 0.9364    | 6.07125               | 13.40573             | 15.52    | 44.55     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_pretrained.pdparams)           |
| ResNet101_vd        | 0.8017    | 0.9497    | 6.11704               | 13.76222             | 16.1     | 44.57     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_pretrained.pdparams)        |
| ResNet152           | 0.7826    | 0.9396    | 8.50198               | 19.17073             | 23.05    | 60.19     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_pretrained.pdparams)           |
| ResNet152_vd        | 0.8059    | 0.9530    | 8.54376               | 19.52157             | 23.53    | 60.21     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_vd_pretrained.pdparams)        |
| ResNet200_vd        | 0.8093    | 0.9533    | 10.80619              | 25.01731             | 30.53    | 74.74     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet200_vd_pretrained.pdparams)        |
| 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/dygraph/ResNet50_vd_ssld_pretrained.pdparams)    |
| 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/dygraph/ResNet50_vd_ssld_v2_pretrained.pdparams) |
| 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/dygraph/ResNet101_vd_ssld_pretrained.pdparams)   |


<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).

| Model                              | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1 | Flops(G) | Params(M) | Model storage size(M) | Download Address                                                                                                      |
|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|
| 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/dygraph/MobileNetV1_x0_25_pretrained.pdparams)                |
| 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/dygraph/MobileNetV1_x0_5_pretrained.pdparams)                 |
| 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/dygraph/MobileNetV1_x0_75_pretrained.pdparams)                |
| MobileNetV1                      | 0.7099    | 0.8968    | 32.523048              | 1.11     | 4.19      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams)                      |
| MobileNetV1_<br>ssld                 | 0.7789    | 0.9394    | 32.523048              | 1.11     | 4.19      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams)                 |
| 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/dygraph/MobileNetV2_x0_25_pretrained.pdparams)                |
| 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/dygraph/MobileNetV2_x0_5_pretrained.pdparams)                 |
| 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/dygraph/MobileNetV2_x0_75_pretrained.pdparams)                |
| MobileNetV2                      | 0.7215    | 0.9065    | 23.317699              | 0.6      | 3.44      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams)                      |
| 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/dygraph/MobileNetV2_x1_5_pretrained.pdparams)                 |
| 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/dygraph/MobileNetV2_x2_0_pretrained.pdparams)                 |
| MobileNetV2_<br>ssld                 | 0.7674    | 0.9339    | 23.317699              | 0.6      | 3.44      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams)                 |
| 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/dygraph/MobileNetV3_large_x1_25_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams)           |
| 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/dygraph/MobileNetV3_large_x0_75_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams)           |
| 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/dygraph/MobileNetV3_large_x0_35_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_small_x1_25_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_small_x1_0_pretrained.pdparams)           |
| 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/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams)           |
| 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/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_small_x0_35_ssld_pretrained.pdparams)          |
| 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/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)      |
| 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/dygraph/MobileNetV3_small_x1_0_ssld_pretrained.pdparams)      |
| ShuffleNetV2                     | 0.6880    | 0.8845    | 10.941                 | 0.28     | 2.26      | 9       | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams)                     |
| 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/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams)               |
| 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/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams)               |
| 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/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams)                |
| 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/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams)                |
| 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/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams)                |
| 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/dygraph/ShuffleNetV2_swish_pretrained.pdparams)               |
| 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/dygraph/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/dygraph/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/dygraph/GhostNet_x1_3_pretrained.pdparams)               |
| GhostNet_<br>x1_3_ssld                    | 0.7938    | 0.9449    | 19.9825                | 0.44     | 7.3       | 29      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_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                                                                                         |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|
| 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/dygraph/Res2Net50_26w_4s_pretrained.pdparams)          |
| 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/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams)       |
| 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/dygraph/Res2Net50_14w_8s_pretrained.pdparams)          |
| 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/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams)      |
| 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/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams)      |
| 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/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
| 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/dygraph/ResNeXt50_32x4d_pretrained.pdparams)           |
| 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/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams)        |
| 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/dygraph/ResNeXt50_64x4d_pretrained.pdparams)           |
| 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/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams)        |
| 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/dygraph/ResNeXt101_32x4d_pretrained.pdparams)          |
| 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/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams)       |
| 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/dygraph/ResNeXt101_64x4d_pretrained.pdparams)          |
| 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/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams)       |
| 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/dygraph/ResNeXt152_32x4d_pretrained.pdparams)          |
| 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/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams)       |
| 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/dygraph/ResNeXt152_64x4d_pretrained.pdparams)          |
| 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/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams)       |
| 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/dygraph/SE_ResNet18_vd_pretrained.pdparams)            |
| 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/dygraph/SE_ResNet34_vd_pretrained.pdparams)            |
| 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/dygraph/SE_ResNet50_vd_pretrained.pdparams)            |
| 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/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams)        |
| 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/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams)     |
| SE_ResNeXt101_<br>32x4d       | 0.7939    | 0.9443    | 18.82604              | 25.31814             | 15.02    | 46.28     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams)       |
| SENet154_vd               | 0.8140    | 0.9548    | 53.79794              | 66.31684             | 45.83    | 114.29    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams)               |


<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                                                                                         |
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|
| DenseNet121 | 0.7566    | 0.9258    | 4.40447               | 9.32623              | 5.69     | 7.98      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams) |
| DenseNet161 | 0.7857    | 0.9414    | 10.39152              | 22.15555             | 15.49    | 28.68     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams) |
| DenseNet169 | 0.7681    | 0.9331    | 6.43598               | 12.98832             | 6.74     | 14.15     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams) |
| DenseNet201 | 0.7763    | 0.9366    | 8.20652               | 17.45838             | 8.61     | 20.01     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams) |
| DenseNet264 | 0.7796    | 0.9385    | 12.14722              | 26.27707             | 11.54    | 33.37     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams) |
| DPN68       | 0.7678    | 0.9343    | 11.64915              | 12.82807             | 4.03     | 10.78     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams)       |
| DPN92       | 0.7985    | 0.9480    | 18.15746              | 23.87545             | 12.54    | 36.29     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams)       |
| DPN98       | 0.8059    | 0.9510    | 21.18196              | 33.23925             | 22.22    | 58.46     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams)       |
| DPN107      | 0.8089    | 0.9532    | 27.62046              | 52.65353             | 35.06    | 82.97     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams)      |
| DPN131      | 0.8070    | 0.9514    | 28.33119              | 46.19439             | 30.51    | 75.36     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams)      |

<a name="HRNet_series"></a>
### HRNet series

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).


| Model         | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                 |
|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|
| 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/dygraph/HRNet_W18_C_pretrained.pdparams) |
| 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/dygraph/HRNet_W18_C_ssld_pretrained.pdparams) |
| 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/dygraph/HRNet_W30_C_pretrained.pdparams) |
| 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/dygraph/HRNet_W32_C_pretrained.pdparams) |
| 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/dygraph/HRNet_W40_C_pretrained.pdparams) |
| 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/dygraph/HRNet_W44_C_pretrained.pdparams) |
| 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/dygraph/HRNet_W48_C_pretrained.pdparams) |
| 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/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) |
| 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/dygraph/HRNet_W64_C_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.8475    |  0.9726    |    31.69770      |      94.99546     | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |


<a name="Inception_series"></a>
### Inception series

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).


| Model                 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                         |
|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|
| GoogLeNet          | 0.7070    | 0.8966    | 1.88038               | 4.48882              | 2.88     | 8.46      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams)          |
| Xception41         | 0.7930    | 0.9453    | 4.96939               | 17.01361             | 16.74    | 22.69     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams)         |
| Xception41_deeplab | 0.7955    | 0.9438    | 5.33541               | 17.55938             | 18.16    | 26.73     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams) |
| Xception65         | 0.8100    | 0.9549    | 7.26158               | 25.88778             | 25.95    | 35.48     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams)         |
| Xception65_deeplab | 0.8032    | 0.9449    | 7.60208               | 26.03699             | 27.37    | 39.52     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams) |
| Xception71         | 0.8111    | 0.9545    | 8.72457               | 31.55549             | 31.77    | 37.28     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams)         |
| InceptionV3        | 0.7914    | 0.9459    | 6.64054              | 13.53630              | 11.46    | 23.83     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams)        |
| InceptionV4        | 0.8077    | 0.9526    | 12.99342              | 25.23416             | 24.57    | 42.68     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams)        |


<a name="EfficientNet_and_ResNeXt101_wsl_series"></a>
### EfficientNet and ResNeXt101_wsl series

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).


| Model                       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                               |
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|
| 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/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams)      |
| 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/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams)     |
| 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/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams)     |
| 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/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams)     |
| 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/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams) |
| EfficientNetB0            | 0.7738    | 0.9331    | 3.442            | 6.11476          | 0.72     | 5.1       | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams)            |
| EfficientNetB1            | 0.7915    | 0.9441    | 5.3322           | 9.41795          | 1.27     | 7.52      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams)            |
| EfficientNetB2            | 0.7985    | 0.9474    | 6.29351          | 10.95702         | 1.85     | 8.81      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams)            |
| EfficientNetB3            | 0.8115    | 0.9541    | 7.67749          | 16.53288         | 3.43     | 11.84     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams)            |
| EfficientNetB4            | 0.8285    | 0.9623    | 12.15894         | 30.94567         | 8.29     | 18.76     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams)            |
| EfficientNetB5            | 0.8362    | 0.9672    | 20.48571         | 61.60252         | 19.51    | 29.61     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams)            |
| EfficientNetB6            | 0.8400    | 0.9688    | 32.62402         | -                | 36.27    | 42        | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams)            |
| EfficientNetB7            | 0.8430    | 0.9689    | 53.93823         | -                | 72.35    | 64.92     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams)            |
| 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/dygraph/EfficientNetB0_small_pretrained.pdparams)      |


<a name="ResNeSt_and_RegNet_series"></a>
### ResNeSt and RegNet series

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).


| Model                    | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                                 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| 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/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
| ResNeSt50              | 0.8083    | 0.9542    | 6.69042    | 8.01664                | 10.78    | 27.5      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/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/dygraph/RegNetX_4GF_pretrained.pdparams)            |


<a name="ViT_and_DeiT"></a>
### ViT and DeiT series

Accuracy and inference time metrics of ViT and DeiT series models are shown as follows. More detailed information can be refered to [ViT and DeiT series tutorial](./docs/en/models/ViT_and_DeiT_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 |
|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|
| ViT_small_<br/>patch16_224 | 0.7769  | 0.9342   | -                | -                |      |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) |
| ViT_base_<br/>patch16_224 | 0.8195   | 0.9617   | -    | -                |     | 86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) |
| ViT_base_<br/>patch16_384 | 0.8414  | 0.9717   |    -              |      -           |         |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) |
| ViT_base_<br/>patch32_384 | 0.8176   | 0.9613   | - | - |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) |
| ViT_large_<br/>patch16_224 | 0.8323  | 0.9650   | - | - |  | 307 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) |
| ViT_large_<br/>patch16_384 | 0.8513  | 0.9736  | - | - |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) |
| ViT_large_<br/>patch32_384 | 0.8153   | 0.9608  | - | - |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) |
|                            |           |           |                  |                  |          |           |                                                              |


| Model                    | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address |
|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|
| DeiT_tiny_<br>patch16_224 | 0.718 | 0.910 | -                | -                |      | 5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) |
| DeiT_small_<br>patch16_224 | 0.796 | 0.949 | -    | -                |     | 22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) |
| DeiT_base_<br>patch16_224 | 0.817 | 0.957 |    -              |      -           |         | 86 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) |
| DeiT_base_<br>patch16_384 | 0.830 | 0.962 | - | - |  | 87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) |
| DeiT_tiny_<br>distilled_patch16_224 | 0.741 | 0.918 | - | - |  | 6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) |
| DeiT_small_<br>distilled_patch16_224 | 0.809 | 0.953 | - | - |  | 22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) |
| DeiT_base_<br>distilled_patch16_224 | 0.831 | 0.964 | - | - |  | 87 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) |
| DeiT_base_<br>distilled_patch16_384 | 0.851 | 0.973 | - | - |  | 88 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) |
|  |  |  |  |  |  |  |  |


<a name="RepVGG_series"></a>

### RepVGG

Accuracy and inference time metrics of RepVGG series models are shown as follows. More detailed information can be refered to [RepVGG series tutorial](./docs/en/models/RepVGG_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 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| RepVGG_A0   | 0.7131    | 0.9016    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) |
| RepVGG_A1   | 0.7380    | 0.9146    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) |
| RepVGG_A2   | 0.7571    | 0.9264    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) |
| RepVGG_B0   | 0.7450    | 0.9213    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) |
| RepVGG_B1   | 0.7773    | 0.9385    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) |
| RepVGG_B2   | 0.7813    | 0.9410    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) |
| RepVGG_B1g2 | 0.7732    | 0.9359    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) |
| RepVGG_B1g4 | 0.7675    | 0.9335    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) |
| RepVGG_B2g4 | 0.7881    | 0.9448    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) |
| RepVGG_B3g4 | 0.7965    | 0.9485    |  |  |  |  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) |

<a name="MixNet"></a>

### MixNet

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

| Model    | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(M) | Params(M) | Download Address                                             |
| -------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| MixNet_S | 0.7628    | 0.9299    |                  |                  | 252.977  | 4.167     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams) |
| MixNet_M | 0.7767    | 0.9364    |                  |                  | 357.119  | 5.065     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams) |
| MixNet_L | 0.7860    | 0.9437    |                  |                  | 579.017  | 7.384     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams) |

<a name="ReXNet"></a>

### ReXNet

Accuracy and inference time metrics of ReXNet series models are shown as follows. More detailed information can be refered to [ReXNet series tutorial](./docs/en/models/ReXNet_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                                             |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| ReXNet_1_0 | 0.7746    | 0.9370    |                  |                  | 0.415    | 4.838     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams) |
| ReXNet_1_3 | 0.7913    | 0.9464    |                  |                  | 0.683    | 7.611     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams) |
| ReXNet_1_5 | 0.8006    | 0.9512    |                  |                  | 0.900    | 9.791     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams) |
| ReXNet_2_0 | 0.8122    | 0.9536    |                  |                  | 1.561    | 16.449    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams) |
| ReXNet_3_0 | 0.8209    | 0.9612    |                  |                  | 3.445    | 34.833    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams) |

<a name="SwinTransformer"></a>

### SwinTransformer

Accuracy and inference time metrics of SwinTransformer series models are shown as follows. More detailed information can be refered to [SwinTransformer series tutorial](./docs/en/models/SwinTransformer_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                                             |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| SwinTransformer_tiny_patch4_window7_224    | 0.8069 | 0.9534 |                  |                  | 4.5  | 28   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) |
| SwinTransformer_small_patch4_window7_224   | 0.8275 | 0.9613 |                  |                  | 8.7  | 50   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) |
| SwinTransformer_base_patch4_window7_224    | 0.8300 | 0.9626 |                  |                  | 15.4 | 88   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) |
| SwinTransformer_base_patch4_window12_384   | 0.8439 | 0.9693 |                  |                  | 47.1 | 88   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) |
| SwinTransformer_base_patch4_window7_224<sup>[1]</sup>     | 0.8487 | 0.9746 |                  |                  | 15.4 | 88   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) |
| SwinTransformer_base_patch4_window12_384<sup>[1]</sup>    | 0.8642 | 0.9807 |                  |                  | 47.1 | 88   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) |
| SwinTransformer_large_patch4_window7_224<sup>[1]</sup>    | 0.8596 | 0.9783 |                  |                  | 34.5 | 197  | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) |
| SwinTransformer_large_patch4_window12_384<sup>[1]</sup>   | 0.8719 | 0.9823 |                  |                  | 103.9 | 197 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) |

[1]: Based on imagenet22k dataset pre-training, and then in imagenet1k dataset transfer learning.

<a name="Others"></a>

### Others

Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series and DarkNet53 models are shown as follows. More detailed information can be refered to [Others](./docs/en/models/Others_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 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| AlexNet       | 0.567 | 0.792 | 1.44993         | 2.46696         | 1.370 | 61.090 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) |
| SqueezeNet1_0 | 0.596 | 0.817 | 0.96736 | 2.53221         | 1.550 | 1.240 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) |
| SqueezeNet1_1 | 0.601 | 0.819 | 0.76032       | 1.877      | 0.690   | 1.230 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) |
| VGG11 | 0.693 | 0.891 | 3.90412 | 9.51147 | 15.090 | 132.850 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams) |
| VGG13 | 0.700 | 0.894 | 4.64684 | 12.61558 | 22.480 | 133.030 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams) |
| VGG16 | 0.720 | 0.907 | 5.61769 | 16.40064 | 30.810 | 138.340 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams) |
| VGG19 | 0.726 | 0.909 | 6.65221 | 20.4334 | 39.130 | 143.650 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams) |
| DarkNet53 | 0.780 | 0.941 | 4.10829 | 12.1714 | 18.580 | 41.600 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) |


<a name="License"></a>
## License

PaddleClas is released under the <a href="https://github.com/PaddlePaddle/PaddleClas/blob/master/LICENSE">Apache 2.0 license</a>


<a name="Contribution"></a>
## Contribution

Contributions are highly welcomed and we would really appreciate your feedback!!

- Thank [nblib](https://github.com/nblib) to fix bug of RandErasing.
- Thank [chenpy228](https://github.com/chenpy228) to fix some typos PaddleClas.
- Thank [jm12138](https://github.com/jm12138) to add ViT, DeiT models and RepVGG models into PaddleClas.