简体中文 | [English](README.md) # PaddleClas ## 简介 飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集，助力使用者训练出更好的视觉模型和应用落地。 **近期更新** - 2020.09.17 添加 `HRNet_W48_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.62%；添加 `ResNet34_vd_ssld `模型，在ImageNet-1k上Top-1 Acc可达79.72%。 - 2020.09.07 添加 `HRNet_W18_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达81.16%；添加 `MobileNetV3_small_x0_35_ssld `模型，在ImageNet-1k上Top-1 Acc可达55.55%。 - 2020.07.14 添加 `Res2Net200_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达85.13%；添加 `Fix_ResNet50_vd_ssld_v2 `模型，在ImageNet-1k上Top-1 Acc可达84.0%。 - 2020.06.17 添加英文文档。 - 2020.06.12 添加对windows和CPU环境的训练与评估支持。 - 2020.05.17 添加混合精度训练，基于 `ResNet50 `模型，精度几乎无损的情况下，训练时间可以减少约40%。 - [more](./docs/zh_CN/update_history.md) ## 特性 - 丰富的模型库：基于ImageNet1k分类数据集，PaddleClas提供了24个系列的分类网络结构和训练配置，122个预训练模型和性能评估。 - SSLD知识蒸馏：基于该方案蒸馏模型的识别准确率普遍提升3%以上。 - 数据增广：支持AutoAugment、Cutout、Cutmix等8种数据增广算法详细介绍、代码复现和在统一实验环境下的效果评估。 - 10万类图像分类预训练模型：百度自研并开源了基于10万类数据集训练的 `ResNet50_vd `模型，在一些实际场景中，使用该预训练模型的识别准确率最多可以提升30%。 - 多种训练方案，包括多机训练、混合精度训练等。 - 多种预测推理、部署方案，包括TensorRT预测、Paddle-Lite预测、模型服务化部署、模型量化、Paddle Hub等。 - 可运行于Linux、Windows、MacOS等多种系统。 ## 文档教程 - [快速安装](./docs/zh_CN/tutorials/install.md) - [30分钟玩转PaddleClas](./docs/zh_CN/tutorials/quick_start.md) - [模型库介绍和预训练模型](./docs/zh_CN/models/models_intro.md) - [模型库概览图](#模型库概览图) - [ResNet及其Vd系列](#ResNet及其Vd系列) - [移动端系列](#移动端系列) - [SEResNeXt与Res2Net系列](#SEResNeXt与Res2Net系列) - [DPN与DenseNet系列](#DPN与DenseNet系列) - [HRNet](HRNet系列) - [Inception系列](#Inception系列) - [EfficientNet与ResNeXt101_wsl系列](#EfficientNet与ResNeXt101_wsl系列) - [ResNeSt与RegNet系列](#ResNeSt与RegNet系列) - 模型训练/评估 - [数据准备](./docs/zh_CN/tutorials/data.md) - [模型训练与微调](./docs/zh_CN/tutorials/getting_started.md) - [模型评估](./docs/zh_CN/tutorials/getting_started.md) - 模型预测 - [基于训练引擎预测推理](./docs/zh_CN/extension/paddle_inference.md) - [基于Python预测引擎预测推理](./docs/zh_CN/extension/paddle_inference.md) - 基于C++预测引擎预测推理(coming soon) - [服务化部署](./docs/zh_CN/extension/paddle_serving.md) - 端侧部署(coming soon) - [模型量化压缩](docs/zh_CN/extension/paddle_quantization.md) - 高阶使用 - [知识蒸馏](./docs/zh_CN/advanced_tutorials/distillation/distillation.md) - [数据增广](./docs/zh_CN/advanced_tutorials/image_augmentation/ImageAugment.md) - 特色拓展应用 - [迁移学习](./docs/zh_CN/application/transfer_learning.md) - [10万类图像分类预训练模型](./docs/zh_CN/application/transfer_learning.md) - [通用目标检测](./docs/zh_CN/application/object_detection.md) - FAQ - 图像分类通用问题(coming soon) - [PaddleClas实战FAQ](./docs/zh_CN/faq.md) - [赛事支持](./docs/zh_CN/competition_support.md) - [许可证书](#许可证书) - [贡献代码](#贡献代码) ## 模型库 ### 模型库概览图 基于ImageNet1k分类数据集，PaddleClas支持24种系列分类网络结构以及对应的122个图像分类预训练模型，训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现，下面所有的速度指标评估环境如下： * CPU的评估环境基于骁龙855（SD855）。 * GPU评估环境基于T4机器，在FP32+TensorRT配置下运行500次测得（去除前10次的warmup时间）。 常见服务器端模型的精度指标与其预测耗时的变化曲线如下图所示。 ![](./docs/images/models/T4_benchmark/t4.fp32.bs4.main_fps_top1.png) 常见移动端模型的精度指标与其预测耗时、模型存储大小的变化曲线如下图所示。 ![](./docs/images/models/mobile_arm_storage.png) ![](./docs/images/models/mobile_arm_top1.png) ### ResNet及其Vd系列 ResNet及其Vd系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNet及其Vd系列模型文档](./docs/zh_CN/models/ResNet_and_vd.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------| | ResNet18 | 0.7098 | 0.8992 | 1.45606 | 3.56305 | 3.66 | 11.69 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | | ResNet50_vd_
ssld | 0.8239 | 0.9610 | 3.53131 | 8.09057 | 8.67 | 25.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar) | | ResNet50_vd_
ssld_v2 | 0.8300 | 0.9640 | 3.53131 | 8.09057 | 8.67 | 25.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_v2_pretrained.tar) | | ResNet101_vd_
ssld | 0.8373 | 0.9669 | 6.11704 | 13.76222 | 16.1 | 44.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_ssld_pretrained.tar) | ### 移动端系列 移动端系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[移动端系列模型文档](./docs/zh_CN/models/Mobile.md)。 | 模型 | Top-1 Acc | Top-5 Acc | SD855 time(ms)
bs=1 | Flops(G) | Params(M) | 模型大小(M) | 下载地址 | |----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------| | MobileNetV1_
x0_25 | 0.5143 | 0.7546 | 3.21985 | 0.07 | 0.46 | 1.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_25_pretrained.tar) | | MobileNetV1_
x0_5 | 0.6352 | 0.8473 | 9.579599 | 0.28 | 1.31 | 5.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_5_pretrained.tar) | | MobileNetV1_
x0_75 | 0.6881 | 0.8823 | 19.436399 | 0.63 | 2.55 | 10 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | | MobileNetV1_
ssld | 0.7789 | 0.9394 | 32.523048 | 1.11 | 4.19 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_ssld_pretrained.tar) | | MobileNetV2_
x0_25 | 0.5321 | 0.7652 | 3.79925 | 0.05 | 1.5 | 6.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_25_pretrained.tar) | | MobileNetV2_
x0_5 | 0.6503 | 0.8572 | 8.7021 | 0.17 | 1.93 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_5_pretrained.tar) | | MobileNetV2_
x0_75 | 0.6983 | 0.8901 | 15.531351 | 0.35 | 2.58 | 10 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | | MobileNetV2_
x1_5 | 0.7412 | 0.9167 | 45.623848 | 1.32 | 6.76 | 26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x1_5_pretrained.tar) | | MobileNetV2_
x2_0 | 0.7523 | 0.9258 | 74.291649 | 2.32 | 11.13 | 43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x2_0_pretrained.tar) | | MobileNetV2_
ssld | 0.7674 | 0.9339 | 23.317699 | 0.6 | 3.44 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_ssld_pretrained.tar) | | MobileNetV3_
large_x1_25 | 0.7641 | 0.9295 | 28.217701 | 0.714 | 7.44 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_25_pretrained.tar) | | MobileNetV3_
large_x1_0 | 0.7532 | 0.9231 | 19.30835 | 0.45 | 5.47 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar) | | MobileNetV3_
large_x0_75 | 0.7314 | 0.9108 | 13.5646 | 0.296 | 3.91 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_75_pretrained.tar) | | MobileNetV3_
large_x0_5 | 0.6924 | 0.8852 | 7.49315 | 0.138 | 2.67 | 11 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar) | | MobileNetV3_
large_x0_35 | 0.6432 | 0.8546 | 5.13695 | 0.077 | 2.1 | 8.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_35_pretrained.tar) | | MobileNetV3_
small_x1_25 | 0.7067 | 0.8951 | 9.2745 | 0.195 | 3.62 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_25_pretrained.tar) | | MobileNetV3_
small_x1_0 | 0.6824 | 0.8806 | 6.5463 | 0.123 | 2.94 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_pretrained.tar) | | MobileNetV3_
small_x0_75 | 0.6602 | 0.8633 | 5.28435 | 0.088 | 2.37 | 9.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_75_pretrained.tar) | | MobileNetV3_
small_x0_5 | 0.5921 | 0.8152 | 3.35165 | 0.043 | 1.9 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_5_pretrained.tar) | | MobileNetV3_
small_x0_35 | 0.5303 | 0.7637 | 2.6352 | 0.026 | 1.66 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_35_pretrained.tar) | | MobileNetV3_
small_x0_35_ssld | 0.5555 | 0.7771 | 2.6352 | 0.026 | 1.66 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x0_35_ssld_pretrained.tar) | | MobileNetV3_
large_x1_0_ssld | 0.7896 | 0.9448 | 19.30835 | 0.45 | 5.47 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar) | | MobileNetV3_large_
x1_0_ssld_int8 | 0.7605 | - | 14.395 | - | - | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_int8_pretrained.tar) | | MobileNetV3_small_
x1_0_ssld | 0.7129 | 0.9010 | 6.5463 | 0.123 | 2.94 | 12 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | | ShuffleNetV2_
x0_25 | 0.4990 | 0.7379 | 2.329 | 0.03 | 0.6 | 2.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x0_25_pretrained.tar) | | ShuffleNetV2_
x0_33 | 0.5373 | 0.7705 | 2.64335 | 0.04 | 0.64 | 2.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x0_33_pretrained.tar) | | ShuffleNetV2_
x0_5 | 0.6032 | 0.8226 | 4.2613 | 0.08 | 1.36 | 5.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x0_5_pretrained.tar) | | ShuffleNetV2_
x1_5 | 0.7163 | 0.9015 | 19.3522 | 0.58 | 3.47 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x1_5_pretrained.tar) | | ShuffleNetV2_
x2_0 | 0.7315 | 0.9120 | 34.770149 | 1.12 | 7.32 | 28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x2_0_pretrained.tar) | | ShuffleNetV2_
swish | 0.7003 | 0.8917 | 16.023151 | 0.29 | 2.26 | 9.1 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DARTS_GS_6M_pretrained.tar) | | GhostNet_
x0_5 | 0.6688 | 0.8695 | 5.7143 | 0.082 | 2.6 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams) | | GhostNet_
x1_0 | 0.7402 | 0.9165 | 13.5587 | 0.294 | 5.2 | 20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams) | | GhostNet_
x1_3 | 0.7579 | 0.9254 | 19.9825 | 0.44 | 7.3 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams) | ### SEResNeXt与Res2Net系列 SEResNeXt与Res2Net系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[SEResNeXt与Res2Net系列模型文档](./docs/zh_CN/models/SEResNext_and_Res2Net.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------| | Res2Net50_
26w_4s | 0.7933 | 0.9457 | 4.47188 | 9.65722 | 8.52 | 25.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_26w_4s_pretrained.tar) | | Res2Net50_vd_
26w_4s | 0.7975 | 0.9491 | 4.52712 | 9.93247 | 8.37 | 25.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_vd_26w_4s_pretrained.tar) | | Res2Net50_
14w_8s | 0.7946 | 0.9470 | 5.4026 | 10.60273 | 9.01 | 25.72 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_14w_8s_pretrained.tar) | | Res2Net101_vd_
26w_4s | 0.8064 | 0.9522 | 8.08729 | 17.31208 | 16.67 | 45.22 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net101_vd_26w_4s_pretrained.tar) | | Res2Net200_vd_
26w_4s | 0.8121 | 0.9571 | 14.67806 | 32.35032 | 31.49 | 76.21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_pretrained.tar) | | Res2Net200_vd_
26w_4s_ssld | 0.8513 | 0.9742 | 14.67806 | 32.35032 | 31.49 | 76.21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_ssld_pretrained.tar) | | ResNeXt50_
32x4d | 0.7775 | 0.9382 | 7.56327 | 10.6134 | 8.02 | 23.64 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | | ResNeXt50_vd_
32x4d | 0.7956 | 0.9462 | 7.62044 | 11.03385 | 8.5 | 23.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_32x4d_pretrained.tar) | | ResNeXt50_
64x4d | 0.7843 | 0.9413 | 13.80962 | 18.4712 | 15.06 | 42.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | | ResNeXt50_vd_
64x4d | 0.8012 | 0.9486 | 13.94449 | 18.88759 | 15.54 | 42.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_64x4d_pretrained.tar) | | ResNeXt101_
32x4d | 0.7865 | 0.9419 | 16.21503 | 19.96568 | 15.01 | 41.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x4d_pretrained.tar) | | ResNeXt101_vd_
32x4d | 0.8033 | 0.9512 | 16.28103 | 20.25611 | 15.49 | 41.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_32x4d_pretrained.tar) | | ResNeXt101_
64x4d | 0.7835 | 0.9452 | 30.4788 | 36.29801 | 29.05 | 78.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar) | | ResNeXt101_vd_
64x4d | 0.8078 | 0.9520 | 30.40456 | 36.77324 | 29.53 | 78.14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | | ResNeXt152_
32x4d | 0.7898 | 0.9433 | 24.86299 | 29.36764 | 22.01 | 56.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_32x4d_pretrained.tar) | | ResNeXt152_vd_
32x4d | 0.8072 | 0.9520 | 25.03258 | 30.08987 | 22.49 | 56.3 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_vd_32x4d_pretrained.tar) | | ResNeXt152_
64x4d | 0.7951 | 0.9471 | 46.7564 | 56.34108 | 43.03 | 107.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_64x4d_pretrained.tar) | | ResNeXt152_vd_
64x4d | 0.8108 | 0.9534 | 47.18638 | 57.16257 | 43.52 | 107.59 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) | | SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | | SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_vd_32x4d_pretrained.tar) | | SE_ResNeXt101_
32x4d | 0.7912 | 0.9420 | 18.82604 | 25.31814 | 15.02 | 46.28 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) | ### DPN与DenseNet系列 DPN与DenseNet系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[DPN与DenseNet系列模型文档](./docs/zh_CN/models/DPN_DenseNet.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------| | DenseNet121 | 0.7566 | 0.9258 | 4.40447 | 9.32623 | 5.69 | 7.98 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet121_pretrained.tar) | | DenseNet161 | 0.7857 | 0.9414 | 10.39152 | 22.15555 | 15.49 | 28.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet161_pretrained.tar) | | DenseNet169 | 0.7681 | 0.9331 | 6.43598 | 12.98832 | 6.74 | 14.15 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet169_pretrained.tar) | | DenseNet201 | 0.7763 | 0.9366 | 8.20652 | 17.45838 | 8.61 | 20.01 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet201_pretrained.tar) | | DenseNet264 | 0.7796 | 0.9385 | 12.14722 | 26.27707 | 11.54 | 33.37 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet264_pretrained.tar) | | DPN68 | 0.7678 | 0.9343 | 11.64915 | 12.82807 | 4.03 | 10.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DPN68_pretrained.tar) | | DPN92 | 0.7985 | 0.9480 | 18.15746 | 23.87545 | 12.54 | 36.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DPN92_pretrained.tar) | | DPN98 | 0.8059 | 0.9510 | 21.18196 | 33.23925 | 22.22 | 58.46 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DPN98_pretrained.tar) | | DPN107 | 0.8089 | 0.9532 | 27.62046 | 52.65353 | 35.06 | 82.97 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DPN107_pretrained.tar) | | DPN131 | 0.8070 | 0.9514 | 28.33119 | 46.19439 | 30.51 | 75.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DPN131_pretrained.tar) | ### HRNet系列 HRNet系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[HRNet系列模型文档](./docs/zh_CN/models/HRNet.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------| | HRNet_W18_C | 0.7692 | 0.9339 | 7.40636 | 13.29752 | 4.14 | 21.29 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W64_C_pretrained.tar) | ### Inception系列 Inception系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[Inception系列模型文档](./docs/zh_CN/models/Inception.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------| | GoogLeNet | 0.7070 | 0.8966 | 1.88038 | 4.48882 | 2.88 | 8.46 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GoogLeNet_pretrained.tar) | | Xception41 | 0.7930 | 0.9453 | 4.96939 | 17.01361 | 16.74 | 22.69 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/Xception71_pretrained.tar) | | InceptionV4 | 0.8077 | 0.9526 | 12.99342 | 25.23416 | 24.57 | 42.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | ### EfficientNet与ResNeXt101_wsl系列 EfficientNet与ResNeXt101_wsl系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[EfficientNet与ResNeXt101_wsl系列模型文档](./docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------| | ResNeXt101_
32x8d_wsl | 0.8255 | 0.9674 | 18.52528 | 34.25319 | 29.14 | 78.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x8d_wsl_pretrained.tar) | | ResNeXt101_
32x16d_wsl | 0.8424 | 0.9726 | 25.60395 | 71.88384 | 57.55 | 152.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x16d_wsl_pretrained.tar) | | ResNeXt101_
32x32d_wsl | 0.8497 | 0.9759 | 54.87396 | 160.04337 | 115.17 | 303.11 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x32d_wsl_pretrained.tar) | | ResNeXt101_
32x48d_wsl | 0.8537 | 0.9769 | 99.01698256 | 315.91261 | 173.58 | 456.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x48d_wsl_pretrained.tar) | | Fix_ResNeXt101_
32x48d_wsl | 0.8626 | 0.9797 | 160.0838242 | 595.99296 | 354.23 | 456.2 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB0_pretrained.tar) | | EfficientNetB1 | 0.7915 | 0.9441 | 5.3322 | 9.41795 | 1.27 | 7.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB1_pretrained.tar) | | EfficientNetB2 | 0.7985 | 0.9474 | 6.29351 | 10.95702 | 1.85 | 8.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB2_pretrained.tar) | | EfficientNetB3 | 0.8115 | 0.9541 | 7.67749 | 16.53288 | 3.43 | 11.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB3_pretrained.tar) | | EfficientNetB4 | 0.8285 | 0.9623 | 12.15894 | 30.94567 | 8.29 | 18.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB4_pretrained.tar) | | EfficientNetB5 | 0.8362 | 0.9672 | 20.48571 | 61.60252 | 19.51 | 29.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB5_pretrained.tar) | | EfficientNetB6 | 0.8400 | 0.9688 | 32.62402 | - | 36.27 | 42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB6_pretrained.tar) | | EfficientNetB7 | 0.8430 | 0.9689 | 53.93823 | - | 72.35 | 64.92 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB7_pretrained.tar) | | EfficientNetB0_
small | 0.7580 | 0.9258 | 2.3076 | 4.71886 | 0.72 | 4.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/EfficientNetB0_small_pretrained.tar) | ### ResNeSt与RegNet系列 ResNeSt与RegNet系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNeSt与RegNet系列模型文档](./docs/zh_CN/models/ResNeSt_RegNet.md)。 | 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 | |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------| | ResNeSt50_
fast_1s1x64d | 0.8035 | 0.9528 | 3.45405 | 8.72680 | 8.68 | 26.3 | [下载链接](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 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams) | | RegNetX_4GF | 0.785 | 0.9416 | 6.46478 | 11.19862 | 8 | 22.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams) | ## 许可证书 本项目的发布受Apache 2.0 license许可认证。 ## 贡献代码 我们非常欢迎你为PaddleClas贡献代码，也十分感谢你的反馈。 - 非常感谢[nblib](https://github.com/nblib)修正了PaddleClas中RandErasing的数据增广配置文件。 - 非常感谢[chenpy228](https://github.com/chenpy228)修正了PaddleClas文档中的部分错别字。