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<!-- 简体中文 | [English](../../en/algorithm_introduction/model_list.md) -->
# ImageNet 预训练模型库
## 目录
- [一、模型库概览图](#Overview)
- [二、SSLD 知识蒸馏预训练模型](#SSLD)
- [2.1 服务器端知识蒸馏模型](#SSLD_server)
- [2.2 移动端知识蒸馏模型](#SSLD_mobile)
- [2.3 Intel CPU 端知识蒸馏模型](#SSLD_intel_cpu)
- [三、CNN 系列模型](#CNN_based)
- [3.1 服务器端模型](#CNN_server)
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- [PP-HGNet & PP-HGNetV2 系列](#PPHGNet)
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- [ResNet 系列](#ResNet)
- [ResNeXt 系列](#ResNeXt)
- [Res2Net 系列](#Res2Net)
- [SENet 系列](#SENet)
- [DPN 系列](#DPN)
- [DenseNet 系列](#DenseNet)
- [HRNet 系列](#HRNet)
- [Inception 系列](#Inception)
- [EfficientNet 系列](#EfficientNet)
- [ResNeXt101_wsl 系列](#ResNeXt101_wsl)
- [ResNeSt 系列](#ResNeSt)
- [RegNet 系列](#RegNet)
- [RepVGG 系列](#RepVGG)
- [MixNet 系列](#MixNet)
- [ReXNet 系列](#ReXNet)
- [HarDNet 系列](#HarDNet)
- [DLA 系列](#DLA)
- [RedNet 系列](#RedNet)
- [ConvNeXt](#ConvNeXt)
- [VAN](#VAN)
- [PeleeNet](#PeleeNet)
- [CSPNet](#CSPNet)
- [其他模型](#Others)
- [3.2 轻量级模型](#CNN_lite)
- [移动端系列](#Mobile)
- [PP-LCNet & PP-LCNetV2 系列](#PPLCNet)
- [四、Transformer 系列模型](#Transformer_based)
- [4.1 服务器端模型](#Transformer_server)
- [ViT 系列](#ViT)
- [DeiT 系列](#DeiT)
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- [SwinTransformer & SwinTransformerV2系列](#SwinTransformer)
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- [Twins 系列](#Twins)
- [CSwinTransformer 系列](#CSwinTransformer)
- [PVTV2 系列](#PVTV2)
- [LeViT 系列](#LeViT)
- [TNT 系列](#TNT)
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- [NextViT 系列](#NextViT)
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- [UniFormer 系列](#UniFormer)
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- [DSNet 系列](#DSNet)
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- [4.2 轻量级模型](#Transformer_lite)
- [MobileViT 系列](#MobileViT)
- [五、参考文献](#reference)
<a name="Overview"></a>
## 一、模型库概览图
基于 ImageNet1k 分类数据集，PaddleClas 支持 37 个系列分类网络结构以及对应的 217 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：
* Arm CPU 的评估环境基于骁龙 855(SD855)。
* Intel CPU 的评估环境基于 Intel(R) Xeon(R) Gold 6148。
* GPU 评估环境基于 V100 机器，在 FP32+TensorRT 配置下运行 2100 次测得（去除前 100 次的 warmup 时间）。
* FLOPs 与 Params 通过 `paddle.flops()` 计算得到（PaddlePaddle 版本为 2.2）
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。
![](../../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
常见移动端模型的精度指标与其预测耗时的变化曲线如下图所示。
![](../../../images/models/mobile_arm_top1.png)
部分VisionTransformer模型的精度指标与其预测耗时的变化曲线如下图所示.
![](../../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
<a name="SSLD"></a>
## 二、SSLD 知识蒸馏预训练模型
基于 SSLD 知识蒸馏的预训练模型列表如下所示，更多关于 SSLD 知识蒸馏方案的介绍可以参考：[SSLD 知识蒸馏文档](../../algorithm_introduction/knowledge_distillation.md)。
<a name="SSLD_server"></a>
### 2.1 服务器端知识蒸馏模型
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|
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| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 1.87 | 2.49 | 4.41 | 3.93 | 21.84 | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar)&emsp;&emsp;</span> |
| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 2.23 | 3.92 | 6.46 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) |
| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 4.04 | 6.84 | 11.44 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) |
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| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 3.59 | 6.35 | 9.50 | 4.28 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_ssld_infer.tar) |
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| Res2Net101_vd_<br>26w_4s_ssld | 0.839 | 0.806 | 0.033 | 5.96 | 10.56 | 15.20 | 8.35 | 45.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net101_vd_26w_4s_ssld_infer.tar) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.851 | 0.812 | 0.049 | 10.79 | 19.48 | 27.95 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) |
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| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 6.33 | 8.12 | 10.91 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) |
| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 10.81 | 15.67 | 25.53 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) |
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| SE_HRNet_W64_C_ssld | 0.848 | - | - | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_HRNet_W64_C_ssld_infer.tar) |
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| PPHGNet_tiny_ssld | 0.8195 | 0.7983 | 0.021 | 1.72 | 3.40 | 5.29 | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
| PPHGNet_small_ssld | 0.8382 | 0.8151 | 0.023 | 2.46 | 5.12 | 8.77 | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
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<a name="SSLD_mobile"></a>
### 2.2 移动端知识蒸馏模型
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | SD855 time(ms)<br>bs=1, thread=1 | SD855 time(ms)<br/>bs=1, thread=2 | SD855 time(ms)<br/>bs=1, thread=4 | FLOPs(M) | Params(M) | <span style="white-space:nowrap;">模型大小(M)</span> | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_ssld_infer.tar) |
| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_ssld_infer.tar) |
| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_ssld_infer.tar) |
| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) |
| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) |
| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_ssld_infer.tar) |
<a name="SSLD_intel_cpu"></a>
### 2.3 Intel CPU 端知识蒸馏模型
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------|-----------------------------------|
| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47.28 | 1.89 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_ssld_infer.tar) |
| PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 160.81 | 2.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_ssld_infer.tar) |
| PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906.49 | 9.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_ssld_infer.tar) |
* 注: `Reference Top-1 Acc` 表示 PaddleClas 基于 ImageNet1k 数据集训练得到的预训练模型精度。
<a name="CNN_based"></a>
## 三、CNN 系列模型
<a name="CNN_server"></a>
### 3.1 服务器端模型
<a name="PPHGNet"></a>
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## PP-HGNet & PP-HGNetV2 系列
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PP-HGNet & PP-HGNetV2 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：[PP-HGNet 系列模型文档](PP-HGNet.md)、[PP-HGNetV2 系列模型文档](PP-HGNetV2.md)。
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| PPHGNet_tiny | 0.7983 | 0.9504 | 1.72 | 3.40 | 5.29 | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar) |
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| PPHGNet_tiny_ssld | 0.8195 | 0.9612 | 1.72 | 3.40 | 5.29 | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
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| PPHGNet_small | 0.8151 | 0.9582 | 2.46 | 5.12 | 8.77 | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar) |
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| PPHGNet_small_ssld | 0.8382 | 0.9681 | 2.46 | 5.12 | 8.77 | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
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| PPHGNet_base_ssld | 0.8500 | 0.9735 | 5.97 | - | - | 25.14 | 71.62 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_base_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_base_ssld_infer.tar) |
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | stage-1预训练模型下载地址 | stage-2预训练模型下载地址 |inference模型下载地址(stage-2) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PPHGNetV2_B0 | 0.7777 | 0.9391 | 0.52 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B0_ssld_stage1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B0_ssld_infer.tar) |
| PPHGNetV2_B1 | 0.7918 | 0.9457 | 0.58 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B1_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B1_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B1_ssld_infer.tar) |
| PPHGNetV2_B2 | 0.8174 | 0.9588 | 0.95 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B2_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B2_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B2_ssld_infer.tar) |
| PPHGNetV2_B3 | 0.8298 | 0.9643 | 1.18 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B3_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B3_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B3_ssld_infer.tar) |
| PPHGNetV2_B4 | 0.8357 | 0.9672 | 1.46 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B4_ssld_infer.tar) |
| PPHGNetV2_B5 | 0.8475 | 0.9732 | 2.84 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B5_ssld_infer.tar) |
| PPHGNetV2_B6 | 0.8630 | 0.9784 | 5.29 | - | - | - | - | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_stage1_pretrained.pdparams)| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNetV2_B6_ssld_infer.tar) |
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<a name="ResNet"></a>
## ResNet 系列 <sup>[[1](#ref1)]</sup>
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 系列模型文档](ResNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
update latency
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| ResNet18 | 0.7098 | 0.8992 | 1.08 | 1.44 | 2.40 | 1.83 | 11.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet18_infer.tar) |
| ResNet18_vd | 0.7226 | 0.9080 | 1.11 | 1.52 | 2.60 | 2.07 | 11.72 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet18_vd_infer.tar) |
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| ResNet34 | 0.7457 | 0.9214 | 1.83 | 2.41 | 4.23 | 3.68 | 21.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_infer.tar) |
| ResNet34_vd | 0.7598 | 0.9298 | 1.87 | 2.49 | 4.41 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_infer.tar) |
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| ResNet34_vd_ssld | 0.7972 | 0.9490 | 1.87 | 2.49 | 4.41 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar) |
update latency
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| ResNet50 | 0.7650 | 0.9300 | 2.19 | 3.77 | 6.22 | 4.11 | 25.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar) |
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| ResNet50_vc | 0.7835 | 0.9403 | 2.57 | 4.83 | 7.52 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vc_infer.tar) |
update latency
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| ResNet50_vd | 0.7912 | 0.9444 | 2.23 | 3.92 | 6.46 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar) |
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| ResNet101 | 0.7756 | 0.9364 | 4.00 | 6.68 | 11.24 | 7.83 | 44.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_infer.tar) |
update latency
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| ResNet101_vd | 0.8017 | 0.9497 | 4.04 | 6.84 | 11.44 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_infer.tar) |
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| ResNet152 | 0.7826 | 0.9396 | 5.71 | 9.58 | 16.16 | 11.56 | 60.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet152_infer.tar) |
| ResNet152_vd | 0.8059 | 0.9530 | 5.76 | 9.75 | 16.40 | 11.80 | 60.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet152_vd_infer.tar) |
update latency
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| ResNet200_vd | 0.8093 | 0.9533 | 7.32 | 12.45 | 21.10 | 15.30 | 74.93 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet200_vd_infer.tar) |
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| ResNet50_vd_<br>ssld | 0.8300 | 0.9640 | 2.23 | 3.92 | 6.46 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) |
| ResNet101_vd_<br>ssld | 0.8373 | 0.9669 | 4.04 | 6.84 | 11.44 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) |
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<a name="ResNeXt"></a>
## ResNeXt 系列 <sup>[[7](#ref7)]</sup>
ResNeXt 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：[ResNeXt 系列模型文档](ResNeXt.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
update latency
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| ResNeXt50_<br>32x4d | 0.7775 | 0.9382 | 2.42 | 8.42 | 11.54 | 4.26 | 25.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_32x4d_infer.tar) |
| ResNeXt50_vd_<br>32x4d | 0.7956 | 0.9462 | 2.50 | 8.62 | 11.90 | 4.50 | 25.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_vd_32x4d_infer.tar) |
| ResNeXt50_<br>64x4d | 0.7843 | 0.9413 | 3.62 | 10.24 | 20.93 | 8.02 | 45.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_64x4d_infer.tar) |
| ResNeXt50_vd_<br>64x4d | 0.8012 | 0.9486 | 3.68 | 10.30 | 21.20 | 8.26 | 45.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_vd_64x4d_infer.tar) |
| ResNeXt101_<br>32x4d | 0.7865 | 0.9419 | 4.81 | 17.60 | 22.98 | 8.01 | 44.32 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x4d_infer.tar) |
| ResNeXt101_vd_<br>32x4d | 0.8033 | 0.9512 | 4.85 | 17.50 | 23.11 | 8.25 | 44.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_vd_32x4d_infer.tar) |
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| ResNeXt101_<br>64x4d | 0.7835 | 0.9452 | 7.12 | 20.17 | 41.63 | 15.52 | 83.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_64x4d_infer.tar) |
update latency
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| ResNeXt101_vd_<br>64x4d | 0.8078 | 0.9520 | 7.34 | 30.30 | 41.79 | 15.76 | 83.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_vd_64x4d_infer.tar) |
| ResNeXt152_<br>32x4d | 0.7898 | 0.9433 | 7.09 | 27.16 | 34.32 | 11.76 | 60.15 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_32x4d_infer.tar) |
| ResNeXt152_vd_<br>32x4d | 0.8072 | 0.9520 | 7.12 | 26.83 | 34.48 | 12.01 | 60.17 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_vd_32x4d_infer.tar) |
| ResNeXt152_<br>64x4d | 0.7951 | 0.9471 | 10.88 | 30.14 | 62.60 | 23.03 | 115.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_64x4d_infer.tar) |
| ResNeXt152_vd_<br>64x4d | 0.8108 | 0.9534 | 10.58 | 30.30 | 62.94 | 23.27 | 115.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_vd_64x4d_infer.tar) |
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<a name="Res2Net"></a>
## Res2Net 系列 <sup>[[9](#ref9)]</sup>
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：[Res2Net 系列模型文档](Res2Net.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
update latency
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| Res2Net50_<br>26w_4s | 0.7933 | 0.9457 | 3.31 | 5.65 | 8.33 | 4.28 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_26w_4s_infer.tar) |
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| Res2Net50_vd_<br>26w_4s | 0.7975 | 0.9491 | 3.35 | 5.79 | 8.63 | 4.52 | 25.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_infer.tar) |
update latency
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| Res2Net50_<br>14w_8s | 0.7946 | 0.9470 | 4.13 | 6.56 | 9.45 | 4.20 | 25.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_14w_8s_infer.tar) |
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| Res2Net101_vd_<br>26w_4s | 0.8064 | 0.9522 | 5.96 | 10.56 | 15.20 | 8.35 | 45.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net101_vd_26w_4s_infer.tar) |
| Res2Net200_vd_<br>26w_4s | 0.8121 | 0.9571 | 10.79 | 19.48 | 27.95 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_infer.tar) |
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| Res2Net200_vd_<br>26w_4s_ssld | 0.8513 | 0.9742 | 10.79 | 19.48 | 27.95 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) |
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<a name="SENet"></a>
## SENet 系列 <sup>[[8](#ref8)]</sup>
SENet 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：[SENet 系列模型文档](SENet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
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| SE_ResNet18_vd | 0.7333 | 0.9138 | 1.31 | 1.77 | 2.92 | 2.07 | 11.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet18_vd_infer.tar) |
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| SE_ResNet34_vd | 0.7651 | 0.9320 | 2.19 | 3.00 | 5.09 | 3.93 | 22.00 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet34_vd_infer.tar) |
| SE_ResNet50_vd | 0.7952 | 0.9475 | 2.72 | 5.07 | 8.12 | 4.36 | 28.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet50_vd_infer.tar) |
| SE_ResNeXt50_<br>32x4d | 0.7844 | 0.9396 | 2.95 | 10.77 | 14.51 | 4.27 | 27.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt50_32x4d_infer.tar) |
| SE_ResNeXt50_vd_<br>32x4d | 0.8024 | 0.9489 | 3.06 | 10.91 | 15.53 | 5.64 | 27.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt50_vd_32x4d_infer.tar) |
| SE_ResNeXt101_<br>32x4d | 0.7939 | 0.9443 | 5.78 | 21.04 | 28.67 | 8.03 | 49.09 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt101_32x4d_infer.tar) |
| SENet154_vd | 0.8140 | 0.9548 | 12.57 | 33.64 | 72.71 | 24.45 | 122.03 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SENet154_vd_infer.tar) |
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<a name="DPN"></a>
## DPN 系列 <sup>[[14](#ref14)]</sup>
DPN 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 系列模型文档](DPN.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|-------------|-------------|
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| DPN68 | 0.7678 | 0.9343 | 2.82 | 10.90 | 14.45 | 2.35 | 12.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN68_infer.tar) |
| DPN92 | 0.7985 | 0.9480 | 4.64 | 11.20 | 20.01 | 6.54 | 37.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN92_infer.tar) |
| DPN98 | 0.8059 | 0.9510 | 6.15 | 25.22 | 35.69 | 11.728 | 61.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN98_infer.tar) |
| DPN107 | 0.8089 | 0.9532 | 8.39 | 34.44 | 52.12 | 18.38 | 87.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN107_infer.tar) |
| DPN131 | 0.8070 | 0.9514 | 8.26 | 33.96 | 48.62 | 16.09 | 79.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN131_infer.tar) |
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<a name="DenseNet"></a>
## DenseNet 系列 <sup>[[15](#ref15)]</sup>
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：[DenseNet 系列模型文档](DenseNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|-------------|-------------|
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| DenseNet121 | 0.7566 | 0.9258 | 3.22 | 6.25 | 8.20 | 2.87 | 8.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet121_infer.tar) |
| DenseNet161 | 0.7857 | 0.9414 | 6.83 | 13.40 | 18.34 | 7.79 | 28.90 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet161_infer.tar) |
| DenseNet169 | 0.7681 | 0.9331 | 4.81 | 9.53 | 11.93 | 3.40 | 14.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet169_infer.tar) |
| DenseNet201 | 0.7763 | 0.9366 | 6.15 | 12.70 | 15.93 | 4.34 | 20.24 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet201_infer.tar) |
| DenseNet264 | 0.7796 | 0.9385 | 9.05 | 19.57 | 23.84 | 5.82 | 33.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet264_infer.tar) |
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<a name="HRNet"></a>
## HRNet 系列 <sup>[[13](#ref13)]</sup>
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](HRNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
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| HRNet_W18_C | 0.7692 | 0.9339 | 6.33 | 8.12 | 10.91 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_infer.tar) |
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| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 6.33 | 8.12 | 10.91 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) |
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| HRNet_W30_C | 0.7804 | 0.9402 | 8.34 | 10.65 | 13.95 | 8.15 | 37.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W30_C_infer.tar) |
| HRNet_W32_C | 0.7828 | 0.9424 | 8.03 | 10.46 | 14.11 | 8.97 | 41.30 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W32_C_infer.tar) |
| HRNet_W40_C | 0.7877 | 0.9447 | 9.64 | 14.27 | 19.54 | 12.74 | 57.64 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W40_C_infer.tar) |
| HRNet_W44_C | 0.7900 | 0.9451 | 10.54 | 15.41 | 24.50 | 14.94 | 67.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W44_C_infer.tar) |
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| HRNet_W48_C | 0.7895 | 0.9442 | 10.81 | 15.67 | 25.53 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_infer.tar) |
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| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 10.81 | 15.67 |25.53 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) |
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| HRNet_W64_C | 0.7930 | 0.9461 | 13.12 | 19.49 | 33.80 | 28.97 | 128.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W64_C_infer.tar) |
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| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_HRNet_W64_C_ssld_infer.tar) |
<a name="Inception"></a>
## Inception 系列 <sup>[[10](#ref10)][[11](#ref11)][[12](#ref12)][[26](#ref26)]</sup>
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](Inception.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
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| GoogLeNet | 0.7070 | 0.8966 | 1.26 | 2.84 | 3.61 | 1.44 | 11.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GoogLeNet_infer.tar) |
| Xception41 | 0.7930 | 0.9453 | 3.20 | 7.78 | 14.83 | 8.57 | 23.02 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception41_infer.tar) |
| Xception41_deeplab | 0.7955 | 0.9438 | 3.34 | 8.22 | 15.54 | 9.28 | 27.08 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception41_deeplab_infer.tar) |
| Xception65 | 0.8100 | 0.9549 | 5.01 | 11.66 | 22.49 | 13.25 | 36.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception65_infer.tar) |
| Xception65_deeplab | 0.8032 | 0.9449 | 4.98 | 11.90 | 22.94 | 13.96 | 40.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception65_deeplab_infer.tar) |
| Xception71 | 0.8111 | 0.9545 | 5.75 | 14.11 | 27.37 | 16.21 | 37.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception71_infer.tar) |
| InceptionV3 | 0.7914 | 0.9459 | 3.92 | 5.98 | 9.57 | 5.73 | 23.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV3_infer.tar) |
| InceptionV4 | 0.8077 | 0.9526 | 7.09 | 10.95 | 18.37 | 12.29 | 42.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV4_infer.tar) |
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<a name="EfficientNet"></a>
## EfficientNet 系列 <sup>[[16](#ref16)]</sup>
EfficientNet 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 系列模型文档](EfficientNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
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| EfficientNetB0 | 0.7738 | 0.9331 | 1.58 | 2.55 | 3.70 | 0.40 | 5.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_infer.tar) |
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| EfficientNetB1 | 0.7915 | 0.9441 | 2.29 | 3.92 | 5.50 | 0.71 | 7.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB1_infer.tar) |
| EfficientNetB2 | 0.7985 | 0.9474 | 2.52 | 4.50 | 6.78 | 1.02 | 9.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB2_infer.tar) |
| EfficientNetB3 | 0.8115 | 0.9541 | 3.44 | 6.53 | 10.44 | 1.88 | 12.324 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB3_infer.tar) |
| EfficientNetB4 | 0.8285 | 0.9623 | 5.35 | 11.69 | 19.97 | 4.51 | 19.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB4_infer.tar) |
| EfficientNetB5 | 0.8362 | 0.9672 | 8.52 | 21.94 | 38.37 | 10.51 | 30.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB5_infer.tar) |
| EfficientNetB6 | 0.8400 | 0.9688 | 13.49 | 37.00 | 67.17 | 19.47 | 43.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB6_infer.tar) |
| EfficientNetB7 | 0.8430 | 0.9689 | 21.91 | 62.69 | 116.07 | 38.45 | 66.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB7_infer.tar) |
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| EfficientNetB0_<br>small | 0.7580 | 0.9258 | 1.24 | 2.59 | 3.92 | 0.40 | 4.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_small_infer.tar) |
<a name="ResNeXt101_wsl"></a>
## ResNeXt101_wsl 系列 <sup>[[17](#ref17)]</sup>
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：[ResNeXt101_wsl 系列模型文档](ResNeXt101_wsl.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
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| ResNeXt101_<br>32x8d_wsl | 0.8255 | 0.9674 | 15.85 | 23.61 | 35.60 | 16.48 | 88.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x8d_wsl_infer.tar) |
| ResNeXt101_<br>32x16d_wsl | 0.8424 | 0.9726 | 20.58 | 37.38 | 66.45 | 36.26 | 194.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x16d_wsl_infer.tar) |
| ResNeXt101_<br>32x32d_wsl | 0.8497 | 0.9759 | 49.87 | 86.16 | 120.14 | 87.28 | 469.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x32d_wsl_infer.tar) |
| ResNeXt101_<br>32x48d_wsl | 0.8537 | 0.9769 | 69.81 | 121.22 | 205.55 | 153.57 | 829.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x48d_wsl_infer.tar) |
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| Fix_ResNeXt101_<br>32x48d_wsl | 0.8626 | 0.9797 | 55.01 | 122.63 | 204.66 | 313.41 | 829.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Fix_ResNeXt101_32x48d_wsl_infer.tar) |
<a name="ResNeSt"></a>
## ResNeSt 系列 <sup>[[24](#ref24)]</sup>
ResNeSt 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 系列模型文档](ResNeSt.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
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| ResNeSt50_<br>fast_1s1x64d | 0.8061 | 0.9527 | 2.42 | 4.34 | 6.96 | 4.36 | 26.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_fast_1s1x64d_infer.tar) |
| ResNeSt50 | 0.8102 | 0.9546 | 13.08 | 16.38 | 23.18 | 5.40 | 27.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_infer.tar) |
| ResNeSt101 | 0.8279 | 0.9642 | 19.16 | 22.62 | 11.24 | 10.25 | 48.40 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt101_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt101_infer.tar) |
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| ResNeSt200 | 0.8418 | 0.9698 | | | | 17.50 | 70.41 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt200_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt200_infer.tar) |
| ResNeSt269 | 0.8444 |0.9698 | | | | 22.54 | 111.23 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt269_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt269_infer.tar) |
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<a name="RegNet"></a>
## RegNet 系列 <sup>[[25](#ref25)]</sup>
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：[RegNet 系列模型文档](RegNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
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| RegNetX_200MF | 0.680 | 0.8842 | 1.00 | 1.29 | 4.12 | 0.20 | 2.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_200MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_200MF_infer.tar) |
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| RegNetX_400MF | 0.723 | 0.9078 | | | | 0.40 | 5.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_400MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_400MF_infer.tar) |
| RegNetX_600MF | 0.737 | 0.9198 | | | | 0.61 | 6.23 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_600MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_600MF_infer.tar) |
| RegNetX_800MF | 0.751 | 0.9250 | | | | 0.81 | 7.30 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_800MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_800MF_infer.tar) |
| RegNetX_1600MF | 0.767 | 0.9329 | | | | 1.62 | 9.23 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_1600MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_1600MF_infer.tar) |
| RegNetX_3200MF | 0.781 | 0.9413 | | | | 3.20 | 15.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_3200MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_3200MF_infer.tar) |
| RegNetX_4GF | 0.785 | 0.9416 | 6.46 | 8.48 | 11.45 | 3.99 | 22.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_4GF_infer.tar) |
| RegNetX_6400MF | 0.790 | 0.9461 | | | | 6.49 | 26.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_6400MF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_6400MF_infer.tar) |
| RegNetX_8GF | 0.793 | 0.9464 | | | | 8.02 | 39.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_8GF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_8GF_infer.tar) |
| RegNetX_12GF | 0.797 | 0.9501 | | | | 12.13 | 46.20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_12GF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_12GF_infer.tar) |
| RegNetX_16GF | 0.801 | 0.9505 | | | | 15.99 | 54.39 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_16GF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_16GF_infer.tar) |
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| RegNetX_32GF | 0.803 | 0.9526 | 13.67 | 28.08 | 51.04 | 32.33 | 130.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_32GF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_32GF_infer.tar) |
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<a name="RepVGG"></a>
## RepVGG 系列 <sup>[[36](#ref36)]</sup>
关于 RepVGG 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RepVGG 系列模型文档](RepVGG.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
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| RepVGG_A0 | 0.7131 | 0.9016 | 1.38 | 1.85 | 2.81 | 1.36 | 8.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A0_infer.tar) |
| RepVGG_A1 | 0.7380 | 0.9146 | 1.68 | 2.33 | 3.70 | 2.37 | 12.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A1_infer.tar) |
| RepVGG_A2 | 0.7571 | 0.9264 | 2.31 | 4.46 | 6.53 | 5.12 | 25.50 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_A2_infer.tar) |
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| RepVGG_B0 | 0.7450 | 0.9213 | 1.99 | 2.87 | 4.67 | 3.06 | 14.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B0_infer.tar) |
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| RepVGG_B1 | 0.7773 | 0.9385 | 3.56 | 7.64 | 13.94 | 11.82 | 51.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1_infer.tar) |
| RepVGG_B2 | 0.7813 | 0.9410 | 4.45 | 9.79 | 19.13 | 18.38 | 80.32 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2_infer.tar) |
| RepVGG_B1g2 | 0.7732 | 0.9359 | 4.18 | 6.93 | 11.99 | 8.82 | 41.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g2_infer.tar) |
| RepVGG_B1g4 | 0.7675 | 0.9335 | 4.72 | 7.23 | 11.14 | 7.31 | 36.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B1g4_infer.tar) |
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| RepVGG_B2g4 | 0.7881 | 0.9448 | 5.47 | 8.94 | 14.73 | 11.34 | 55.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B2g4_infer.tar) |
| RepVGG_B3 | 0.8031 | 0.9517 | 4.28 | 11.64 | 21.14 | 29.16 | 123.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B3_infer.tar) |
| RepVGG_B3g4 | 0.8005 | 0.9502 | 4.21 | 8.22 | 14.68 | 17.89 | 83.93 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_B3g4_infer.tar) |
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| RepVGG_D2se | 0.8339 | 0.9665 | | | | 36.54 | 133.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_D2se_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RepVGG_D2se_infer.tar) |
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<a name="MixNet"></a>
## MixNet 系列 <sup>[[29](#ref29)]</sup>
关于 MixNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MixNet 系列模型文档](MixNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| -------- | --------- | --------- | ---------------- | ---------------- | ----------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| MixNet_S | 0.7628 | 0.9299 | 1.83 | 2.59 | 3.86 | 252.977 | 4.167 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_S_infer.tar) |
| MixNet_M | 0.7767 | 0.9364 | 2.25 | 3.38 | 5.06 | 357.119 | 5.065 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_M_infer.tar) |
| MixNet_L | 0.7860 | 0.9437 | 2.39 | 4.19 | 6.29 | 579.017 | 7.384 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_L_infer.tar) |
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<a name="ReXNet"></a>
## ReXNet 系列 <sup>[[30](#ref30)]</sup>
关于 ReXNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[ReXNet 系列模型文档](ReXNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| ReXNet_1_0 | 0.7746 | 0.9370 | 3.10 | 3.29 | 3.50 | 0.415 | 4.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_0_infer.tar) |
| ReXNet_1_3 | 0.7913 | 0.9464 | 3.38 | 3.45 | 4.37 | 0.68 | 7.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_3_infer.tar) |
| ReXNet_1_5 | 0.8006 | 0.9512 | 3.20 | 3.57 | 6.50 | 0.90 | 9.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_5_infer.tar) |
| ReXNet_2_0 | 0.8122 | 0.9536 | 3.32 | 4.45 | 6.50 | 1.56 | 16.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_2_0_infer.tar) |
| ReXNet_3_0 | 0.8209 | 0.9612 |3.83 | 6.81 | 10.42 | 3.44 | 34.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_3_0_infer.tar) |
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<a name="HarDNet"></a>
## HarDNet 系列 <sup>[[37](#ref37)]</sup>
关于 HarDNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[HarDNet 系列模型文档](HarDNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| HarDNet39_ds | 0.7133 |0.8998 | 1.12 | 1.54 | 2.00 | 0.44 | 3.51 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet39_ds_infer.tar) |
| HarDNet68_ds |0.7362 | 0.9152 | 1.88 | 2.56 | 3.37 | 0.79 | 4.20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet68_ds_infer.tar) |
| HarDNet68| 0.7546 | 0.9265 | 2.97 | 4.12 | 6.05 | 4.26 | 17.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet68_infer.tar) |
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| HarDNet85 | 0.7744 | 0.9355 | 4.67 | 7.17 | 10.85 | 9.09 | 36.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet85_infer.tar) |
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<a name="DLA"></a>
## DLA 系列 <sup>[[38](#ref38)]</sup>
关于 DLA 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[DLA 系列模型文档](DLA.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| DLA102 | 0.7893 |0.9452 | 4.15 | 6.81 | 11.60 | 7.19 | 33.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102_infer.tar) |
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| DLA102x2 |0.7885 | 0.9445 | 6.40 | 16.80 | 33.51 | 9.34 | 41.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102x2_infer.tar) |
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| DLA102x| 0.781 | 0.9400 | 4.68 | 16.44 | 20.98 | 5.89 | 26.40 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102x_infer.tar) |
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| DLA169 | 0.7809 | 0.9409 | 6.45 | 10.79 | 18.31 | 11.59 | 53.50 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA169_infer.tar) |
| DLA34 | 0.7603 | 0.9298 | 1.67 | 2.49 | 4.31 | 3.07 | 15.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA34_infer.tar) |
| DLA46_c |0.6321 | 0.853 | 0.88 | 1.44 | 1.96 | 0.54 | 1.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA46_c_infer.tar) |
| DLA60 | 0.7610 | 0.9292 | 2.54 | 4.26 | 7.01 | 4.26 | 22.08 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60_infer.tar) |
| DLA60x_c | 0.6645 | 0.8754 | 1.04 | 1.82 | 3.68 | 0.59 | 1.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60x_c_infer.tar) |
| DLA60x | 0.7753 | 0.9378 | 2.66 | 8.44 | 11.95 | 3.54 | 17.41 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60x_infer.tar) |
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<a name="RedNet"></a>
## RedNet 系列 <sup>[[39](#ref39)]</sup>
关于 RedNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RedNet 系列模型文档](RedNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| RedNet26 | 0.7595 |0.9319 | 5.36 | 17.89 | 31.83 | 1.69 | 9.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet26_infer.tar) |
| RedNet38 |0.7747 | 0.9356 | 7.42 | 25.11 | 45.99 | 2.14 | 12.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet38_infer.tar) |
| RedNet50| 0.7833 | 0.9417 | 9.47 | 31.93 | 61.41 | 2.61 | 15.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet50_infer.tar) |
| RedNet101 | 0.7894 | 0.9436 | 14.89 | 51.40 | 98.07 | 4.59 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet101_infer.tar) |
| RedNet152 | 0.7917 | 0.9440 | 21.41 | 74.07 | 138.91 | 6.57 | 34.14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet152_infer.tar) |
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<a name="ConvNeXt"></a>
## ConvNeXt 系列 <sup>[[43](#ref43)]</sup>
关于 ConvNeXt 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[ConvNeXt 系列模型文档](ConvNeXt.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| ConvNeXt_tiny | 0.8203 | 0.9590 | - | - | - | 4.458 | 28.583 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ConvNeXt_tiny_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ConvNeXt_tiny_infer.tar) |
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| ConvNeXt_small | 0.8313 | 0.9643 | - | - | - | 8.688 | 50.210 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ConvNeXt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ConvNeXt_small_infer.tar) |
| ConvNeXt_base_224 | 0.8384 | 0.9676 | - | - | - | 15.360 | 88.573 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ConvNeXt_base_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ConvNeXt_base_224_infer.tar) |
| ConvNeXt_base_384 | 0.8490 | 0.9727 | - | - | - | 45.138 | 88.573 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ConvNeXt_base_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ConvNeXt_base_384_infer.tar) |
| ConvNeXt_large_224 | 0.8426 | 0.9690 | - | - | - | 34.340 | 197.740 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ConvNeXt_large_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ConvNeXt_large_224_infer.tar) |
| ConvNeXt_large_384 | 0.8527 | 0.9749 | - | - | - | 101.001 | 197.740 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ConvNeXt_large_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ConvNeXt_large_384_infer.tar) |
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<a name="VAN"></a>
## VAN 系列 <sup>[[44](#ref44)]</sup>
关于 VAN 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[VAN 系列模型文档](VAN.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| VAN_B0 | 0.7535 | 0.9299 | 9.58 | 10.21 | 10.78 | 0.880 | 4.110 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VAN_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VAN_B0_infer.tar) |
| VAN_B1 | 0.8102 | 0.9562 | 8.24 | 8.74 | 9.85 | 2.518 | 13.869 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VAN_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VAN_B1_infer.tar) |
| VAN_B2 | 0.8280 | 0.9620 | 17.09 | 18.48 | 19.32 | 5.032 | 26.592 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VAN_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VAN_B2_infer.tar) |
| VAN_B3 | 0.8389 | 0.9668 | 32.09 | 33.91 | 36.13 | 8.987 | 44.790 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VAN_B3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VAN_B3_infer.tar) |
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<a name="PeleeNet"></a>
## PeleeNet 系列 <sup>[[45](#ref45)]</sup>
关于 PeleeNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[PeleeNet 系列模型文档](PeleeNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
update latency
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| PeleeNet | 0.7153 | 0.9040 | 1.26 | 2.10 | 2.47 | 0.514 | 2.812 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PeleeNet_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PeleeNet_infer.tar) |
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<a name="CSPNet"></a>
## CSPNet 系列 <sup>[[46](#ref46)]</sup>
关于 CSPNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[CSPNet 系列模型文档](CSPNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| CSPDarkNet53 | 0.7725 | 0.9355 | 2.80 | 5.43 | 9.48 | 5.041 | 27.678 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSPDarkNet53_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSPDarkNet53_infer.tar) |
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<a name="Others"></a>
## 其他模型
关于 AlexNet <sup>[[18](#ref18)]</sup>、SqueezeNet 系列 <sup>[[19](#ref19)]</sup>、VGG 系列 <sup>[[20](#ref20)]</sup>、DarkNet53 <sup>[[21](#ref21)]</sup> 等模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](Others.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
update latency
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| AlexNet | 0.567 | 0.792 | 0.64 | 8.88 | 1.21 | 0.71 | 61.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/AlexNet_infer.tar) |
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| SqueezeNet1_0 | 0.596 | 0.817 | 0.68 | 1.64 | 2.62 | 0.78 | 1.25 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_0_infer.tar) |
| SqueezeNet1_1 | 0.601 | 0.819 | 0.62 | 1.30 | 2.09 | 0.35 | 1.24 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_1_infer.tar) |
update latency
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| VGG11 | 0.693 | 0.891 | 1.54 | 3.71 | 6.64 | 7.61 | 132.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG11_infer.tar) |
| VGG13 | 0.700 | 0.894 | 1.83 | 4.96 | 9.16 | 11.31 | 133.05 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG13_infer.tar) |
| VGG16 | 0.720 | 0.907 | 2.28 | 6.56 | 12.25 | 15.470 | 138.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG16_infer.tar) |
| VGG19 | 0.726 | 0.909 | 2.73 | 8.18 | 15.33 | 19.63 | 143.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG19_infer.tar) |
| DarkNet53 | 0.780 | 0.941 | 2.40 | 5.51 | 9.56 | 9.31 | 41.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DarkNet53_infer.tar) |
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<a name="CNN_lite"></a>
### 3.2 轻量级模型
<a name="Mobile"></a>
## 移动端系列 <sup>[[3](#ref3)][[4](#ref4)][[5](#ref5)][[6](#ref6)][[23](#ref23)]</sup>
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：[MobileNetV1 系列模型文档](MobileNetV1.md)、[MobileNetV2 系列模型文档](MobileNetV2.md)、[MobileNetV3 系列模型文档](MobileNetV3.md)、[ShuffleNetV2 系列模型文档](ShuffleNetV2.md)、[GhostNet 系列模型文档](GhostNet.md)、[ESNet 系列模型文档](ESNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1, thread=1 | SD855 time(ms)<br/>bs=1, thread=2 | SD855 time(ms)<br/>bs=1, thread=4 | FLOPs(M) | Params(M) | <span style="white-space:nowrap;">模型大小(M)</span> | 预训练模型下载地址 | inference模型下载地址 |
|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| MobileNetV1_<br>x0_25 | 0.5143 | 0.7546 | 2.88 | 1.82 | 1.26 | 43.56 | 0.48 | 1.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_25_infer.tar) |
| MobileNetV1_<br>x0_5 | 0.6352 | 0.8473 | 8.74 | 5.26 | 3.09 | 154.57 | 1.34 | 5.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_5_infer.tar) |
| MobileNetV1_<br>x0_75 | 0.6881 | 0.8823 | 17.84 | 10.61 | 6.21 | 333.00 | 2.60 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_75_infer.tar) |
| MobileNetV1 | 0.7099 | 0.8968 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar) |
| MobileNetV1_<br>ssld | 0.7789 | 0.9394 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_ssld_infer.tar) |
| MobileNetV2_<br>x0_25 | 0.5321 | 0.7652 | 3.46 | 2.51 | 2.03 | 34.18 | 1.53 | 6.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_25_infer.tar) |
| MobileNetV2_<br>x0_5 | 0.6503 | 0.8572 | 7.69 | 4.92 | 3.57 | 99.48 | 1.98 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_5_infer.tar) |
| MobileNetV2_<br>x0_75 | 0.6983 | 0.8901 | 13.69 | 8.60 | 5.82 | 197.37 | 2.65 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_75_infer.tar) |
| MobileNetV2 | 0.7215 | 0.9065 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_infer.tar) |
| MobileNetV2_<br>x1_5 | 0.7412 | 0.9167 | 40.79 | 24.49 | 15.50 | 702.35 | 6.90 | 26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x1_5_infer.tar) |
| MobileNetV2_<br>x2_0 | 0.7523 | 0.9258 | 67.50 | 40.03 | 25.55 | 1217.25 | 11.33 | 43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x2_0_infer.tar) |
| MobileNetV2_<br>ssld | 0.7674 | 0.9339 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_ssld_infer.tar) |
| MobileNetV3_<br>large_x1_25 | 0.7641 | 0.9295 | 24.52 | 14.76 | 9.89 | 362.70 | 7.47 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_25_infer.tar) |
| MobileNetV3_<br>large_x1_0 | 0.7532 | 0.9231 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar) |
| MobileNetV3_<br>large_x0_75 | 0.7314 | 0.9108 | 11.53 | 7.06 | 4.94 | 151.70 | 3.93 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_75_infer.tar) |
| MobileNetV3_<br>large_x0_5 | 0.6924 | 0.8852 | 6.50 | 4.22 | 3.15 | 71.83 | 2.69 | 11 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_5_infer.tar) |
| MobileNetV3_<br>large_x0_35 | 0.6432 | 0.8546 | 4.43 | 3.11 | 2.41 | 40.90 | 2.11 | 8.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_35_infer.tar) |
| MobileNetV3_<br>small_x1_25 | 0.7067 | 0.8951 | 7.88 | 4.91 | 3.45 | 100.07 | 3.64 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_25_infer.tar) |
| MobileNetV3_<br>small_x1_0 | 0.6824 | 0.8806 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_infer.tar) |
| MobileNetV3_<br>small_x0_75 | 0.6602 | 0.8633 | 4.50 | 2.96 | 2.19 | 46.02 | 2.38 | 9.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_75_infer.tar) |
| MobileNetV3_<br>small_x0_5 | 0.5921 | 0.8152 | 2.89 | 2.04 | 1.62 | 22.60 | 1.91 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_5_infer.tar) |
| MobileNetV3_<br>small_x0_35 | 0.5303 | 0.7637 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_infer.tar) |
| MobileNetV3_<br>small_x0_35_ssld | 0.5555 | 0.7771 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_ssld_infer.tar) |
| MobileNetV3_<br>large_x1_0_ssld | 0.7896 | 0.9448 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) |
| MobileNetV3_small_<br>x1_0_ssld | 0.7129 | 0.9010 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) |
| ShuffleNetV2 | 0.6880 | 0.8845 | 9.72 | 5.97 | 4.13 | 148.86 | 2.29 | 9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_0_infer.tar) |
| ShuffleNetV2_<br>x0_25 | 0.4990 | 0.7379 | 1.94 | 1.53 | 1.43 | 18.95 | 0.61 | 2.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_25_infer.tar) |
| ShuffleNetV2_<br>x0_33 | 0.5373 | 0.7705 | 2.23 | 1.70 | 1.79 | 24.04 | 0.65 | 2.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_33_infer.tar) |
| ShuffleNetV2_<br>x0_5 | 0.6032 | 0.8226 | 3.67 | 2.63 | 2.06 | 42.58 | 1.37 | 5.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_5_infer.tar) |
| ShuffleNetV2_<br>x1_5 | 0.7163 | 0.9015 | 17.21 | 10.56 | 6.81 | 301.35 | 3.53 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_5_infer.tar) |
| ShuffleNetV2_<br>x2_0 | 0.7315 | 0.9120 | 31.21 | 18.98 | 11.65 | 571.70 | 7.40 | 28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x2_0_infer.tar) |
| ShuffleNetV2_<br>swish | 0.7003 | 0.8917 | 31.21 | 9.06 | 5.74 | 148.86 | 2.29 | 9.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_swish_infer.tar) |
| GhostNet_<br>x0_5 | 0.6688 | 0.8695 | 5.28 | 3.95 | 3.29 | 46.15 | 2.60 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x0_5_infer.tar) |
| GhostNet_<br>x1_0 | 0.7402 | 0.9165 | 12.89 | 8.66 | 6.72 | 148.78 | 5.21 | 20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_0_infer.tar) |
| GhostNet_<br>x1_3 | 0.7579 | 0.9254 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_infer.tar) |
| GhostNet_<br>x1_3_ssld | 0.7938 | 0.9449 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_ssld_infer.tar) |
| ESNet_x0_25 | 0.6248 | 0.8346 |4.12|2.97|2.51| 30.85 | 2.83 | 11 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_25_infer.tar) |
| ESNet_x0_5 | 0.6882 | 0.8804 |6.45|4.42|3.35| 67.31 | 3.25 | 13 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_5_infer.tar) |
| ESNet_x0_75 | 0.7224 | 0.9045 |9.59|6.28|4.52| 123.74 | 3.87 | 15 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_75_infer.tar) |
| ESNet_x1_0 | 0.7392 | 0.9140 |13.67|8.71|5.97| 197.33 | 4.64 | 18 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x1_0_infer.tar) |
<a name="PPLCNet"></a>
## PP-LCNet & PP-LCNetV2 系列 <sup>[[28](#ref28)]</sup>
PP-LCNet 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：[PP-LCNet 系列模型文档](PP-LCNet.md)，[PP-LCNetV2 系列模型文档](PP-LCNetV2.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<sup>*</sup><br>bs=1 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|:--:|:--:|:--:|:--:|----|----|----|:--:|
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| PPLCNet_x0_25 |0.5186 | 0.7565 | 0.44 | 18.25 | 1.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar) |
| PPLCNet_x0_35 |0.5809 | 0.8083 | 0.45 | 29.46 | 1.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar) |
| PPLCNet_x0_5 |0.6314 | 0.8466 | 0.44 | 47.28 | 1.89 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar) |
| PPLCNet_x0_75 |0.6818 | 0.8830 | 0.44 | 98.82 | 2.37 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar) |
| PPLCNet_x1_0 |0.7132 | 0.9003 | 0.47 | 160.81 | 2.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar) |
| PPLCNet_x1_5 |0.7371 | 0.9153 | 0.54 | 341.86 | 4.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar) |
| PPLCNet_x2_0 |0.7518 | 0.9227 | 0.64 | 590 | 6.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar) |
| PPLCNet_x2_5 |0.7660 | 0.9300 | 0.71 | 906 | 9.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar) |
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<sup>**</sup><br>bs=1 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|:--:|:--:|:--:|:--:|----|----|----|:--:|
update latency
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| PPLCNetV2_base | 77.04 | 93.27 | 0.67 | 604 | 6.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar) |
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*: 基于 Intel-Xeon-Gold-6148 硬件平台与 PaddlePaddle 推理平台。
**: 基于 Intel-Xeon-Gold-6271C 硬件平台与 OpenVINO 2021.4.2 推理平台。
<a name="Transformer_based"></a>
### 四、Transformer 系列模型
<a name="Transformer_server"></a>
### 4.1 服务器端模型
<a name="ViT"></a>
## ViT 系列 <sup>[[31](#ref31)]</sup>
ViT(Vision Transformer) 系列模型的精度、速度指标如下表所示. 更多关于该系列模型的介绍可以参考： [ViT 系列模型文档](ViT.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------|
update latency
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| ViT_small_<br/>patch16_224 | 0.7769 | 0.9342 | 3.81 | 8.65 | 15.80 | 9.41 | 48.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) |
modify latency
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| ViT_base_<br/>patch16_224 | 0.8195 | 0.9617 | 5.93 | 15.46 | 27.14 | 16.85 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_224_infer.tar) |
update latency
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| ViT_base_<br/>patch16_384 | 0.8414 | 0.9717 | 13.78 | 45.59 | 88.65 | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_384_infer.tar) |
| ViT_base_<br/>patch32_384 | 0.8176 | 0.9613 | 5.29 | 12.33 | 22.44 | 12.66 | 88.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch32_384_infer.tar) |
| ViT_large_<br/>patch16_224 | 0.8323 | 0.9650 | 15.57 | 49.66 | 91.45 | 59.65 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_224_infer.tar) |
|ViT_large_<br/>patch16_384| 0.8513 | 0.9736 | 38.67 | 142.57 | 282.87 | 174.70 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_384_infer.tar) |
|ViT_large_<br/>patch32_384| 0.8153 | 0.9608 | 12.07 | 34.53 | 65.81 | 44.24 | 306.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch32_384_infer.tar) |
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<a name="DeiT"></a>
## DeiT 系列 <sup>[[32](#ref32)]</sup>
DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标如下表所示. 更多关于该系列模型的介绍可以参考： [DeiT 系列模型文档](DeiT.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------|
modify latency
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| DeiT_tiny_<br>patch16_224 | 0.718 | 0.910 | 3.87 | 3.58 | 4.64 | 1.07 | 5.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) |
update latency
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| DeiT_small_<br>patch16_224 | 0.796 | 0.949 | 3.52 | 5.90 | 9.44 | 4.24 | 21.97 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_patch16_224_infer.tar) |
| DeiT_base_<br>patch16_224 | 0.817 | 0.957 | 5.97 | 15.52 | 27.38 | 16.85 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_224_infer.tar) |
| DeiT_base_<br>patch16_384 | 0.830 | 0.962 | 13.78 | 45.94 | 89.38 | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_384_infer.tar) |
| DeiT_tiny_<br>distilled_patch16_224 | 0.741 | 0.918 | 3.31 | 3.61 | 4.57 | 1.08 | 5.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_distilled_patch16_224_infer.tar) |
| DeiT_small_<br>distilled_patch16_224 | 0.809 | 0.953 | 3.57 | 5.91 | 9.51 | 4.26 | 22.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_distilled_patch16_224_infer.tar) |
| DeiT_base_<br>distilled_patch16_224 | 0.831 | 0.964 | 6.00 | 15.43 | 27.10 | 16.93 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_224_infer.tar) |
| DeiT_base_<br>distilled_patch16_384 | 0.851 | 0.973 | 13.76 | 45.61 | 89.15 | 49.43 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_384_infer.tar) |
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<a name="SwinTransformer"></a>
add docs
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## SwinTransformer & SwinTransformerV2 系列 <sup>[[27](#ref27)]</sup><sup>[[50](#ref50)]</sup>
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关于 SwinTransformer 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[SwinTransformer 系列模型文档](SwinTransformer.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
modify README.md
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关于 SwinTransformerV2 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[SwinTransformerV2 系列模型文档](SwinTransformerV2.md)
add docs
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| SwinTransformerV2_tiny_patch4_window8_256 | 0.8177 | 0.9588 | - | - | - | 4.34 | 21.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_tiny_patch4_window8_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_tiny_patch4_window8_256_infer.tar) |
| SwinTransformerV2_tiny_patch4_window16_256 | 0.8283 | 0.9623 | - | - | - | 4.38 | 21.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_tiny_patch4_window16_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_tiny_patch4_window16_256_infer.tar) |
| SwinTransformerV2_small_patch4_window8_256 | 0.8373 | 0.9662 | - | - | - | 8.44 | 37.93 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_small_patch4_window8_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_small_patch4_window8_256_infer.tar) |
| SwinTransformerV2_small_patch4_window16_256 | 0.8414 | 0.9681 | - | - | - | 8.54 | 37.93 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_small_patch4_window16_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_small_patch4_window16_256_infer.tar) |
| SwinTransformerV2_base_patch4_window8_256 | 0.8419 | 0.9687 | - | - | - | 14.97 | 66.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_base_patch4_window8_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_base_patch4_window8_256_infer.tar) |
| SwinTransformerV2_base_patch4_window16_256 | 0.8458 | 0.9706 | - | - | - | 15.11 | 66.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_base_patch4_window16_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_base_patch4_window16_256_infer.tar) |
| SwinTransformerV2_base_patch4_window24_384<sup>[1]</sup> | 0.8714 | 0.9824 | - | - | - | 34.00 | 66.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_base_patch4_window24_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_base_patch4_window24_384_22kto1k_infer.tar) |
| SwinTransformerV2_large_patch4_window16_256<sup>[1]</sup> | 0.8689 | 0.9804 | - | - | - | 33.82 | 149.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_large_patch4_window16_256_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_large_patch4_window16_256_22kto1k_infer.tar) |
| SwinTransformerV2_large_patch4_window24_384<sup>[1]</sup> | 0.8747 | 0.9827 | - | - | - | 76.12 | 149.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformerV2_large_patch4_window24_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformerV2_large_patch4_window24_384_22kto1k_infer.tar) |
docs: fix invalid links
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[1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
<a name="Twins"></a>
## Twins 系列 <sup>[[34](#ref34)]</sup>
关于 Twins 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[Twins 系列模型文档](Twins.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
update latency
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| pcpvt_small | 0.8082 | 0.9552 | 5.74 | 10.51 | 15.27 |3.67 | 24.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_small_infer.tar) |
| pcpvt_base | 0.8242 | 0.9619 | 8.44 | 16.22 | 23.16 | 6.44 | 43.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_base_infer.tar) |
| pcpvt_large | 0.8273 | 0.9650 | 9.28 | 18.72 | 31.18 | 9.50 | 60.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_large_infer.tar) |
modify latency
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| alt_gvt_small | 0.8140 | 0.9546 | 4.93 | - | 10.02 |2.81 | 24.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_small_infer.tar) |
update latency
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| alt_gvt_base | 0.8294 | 0.9621 | 7.48 | 12.60 | 19.93 | 8.34 | 56.07 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_base_infer.tar) |
modify latency
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| alt_gvt_large | 0.8331 | 0.9642 | 9.28 | 18.72 | 31.18 | 14.81 | 99.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_large_infer.tar) |
docs: fix invalid links
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**注**：与 Reference 的精度差异源于数据预处理不同。
<a name="CSWinTransformer"></a>
## CSWinTransformer 系列 <sup>[[40](#ref40)]</sup>
关于 CSWinTransformer 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[CSWinTransformer 系列模型文档](CSWinTransformer.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| CSWinTransformer_tiny_224 | 0.8281 | 0.9628 | - | - | - | 4.1 | 22 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_tiny_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSWinTransformer_tiny_224_infer.tar) |
| CSWinTransformer_small_224 | 0.8358 | 0.9658 | - | - | - | 6.4 | 35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_small_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSWinTransformer_small_224_infer.tar) |
| CSWinTransformer_base_224 | 0.8420 | 0.9692 | - | - | - | 14.3 | 77 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_base_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSWinTransformer_base_224_infer.tar) |
| CSWinTransformer_large_224 | 0.8643 | 0.9799 | - | - | - | 32.2 | 173.3 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_large_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSWinTransformer_large_224_infer.tar) |
| CSWinTransformer_base_384 | 0.8550 | 0.9749 | - | - |- | 42.2 | 77 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_base_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSWinTransformer_base_384_infer.tar) |
| CSWinTransformer_large_384 | 0.8748 | 0.9833 | - | - | - | 94.7 | 173.3 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_large_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSWinTransformer_large_384_infer.tar) |
<a name="PVTV2"></a>
## PVTV2 系列 <sup>[[41](#ref41)]</sup>
关于 PVTV2 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[PVTV2 系列模型文档](PVTV2.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
update latency
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| PVT_V2_B0 | 0.7052 | 0.9016 | 2.87 | 3.46 | - | 0.53 | 3.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B0_infer.tar) |
| PVT_V2_B1 | 0.7869 | 0.9450 | 3.32 | 5.48 | - | 2.0 | 14.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B1_infer.tar) |
| PVT_V2_B2 | 0.8206 | 0.9599 | 5.94 | 9.98 | - | 3.9 | 25.4 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B2_infer.tar) |
docs: fix invalid links
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| PVT_V2_B2_Linear | 0.8205 | 0.9605 | - | - | - | 3.8 | 22.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_Linear_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B2_Linear_infer.tar) |
update latency
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| PVT_V2_B3 | 0.8310 | 0.9648 | 9.46 | - |- | 6.7 | 45.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B3_infer.tar) |
modify latency
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| PVT_V2_B4 | 0.8361 | 0.9666 | 14.06 | - | - | 9.8 | 62.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B4_infer.tar) |
docs: fix invalid links
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| PVT_V2_B5 | 0.8374 | 0.9662 | - | - | - | 11.4 | 82.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B5_infer.tar) |
<a name="LeViT"></a>
## LeViT 系列 <sup>[[33](#ref33)]</sup>
关于 LeViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[LeViT 系列模型文档](LeViT.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| LeViT_128S | 0.7598 | 0.9269 | | | | 281 | 7.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128S_infer.tar) |
| LeViT_128 | 0.7810 | 0.9371 | | | | 365 | 8.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
| LeViT_192 | 0.7934 | 0.9446 | | | | 597 | 10.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) |
| LeViT_256 | 0.8085 | 0.9497 | | | | 1049 | 18.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) |
| LeViT_384 | 0.8191 | 0.9551 | | | | 2234 | 38.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_384_infer.tar) |
**注**：与 Reference 的精度差异源于数据预处理不同及未使用蒸馏的 head 作为输出。
<a name="TNT"></a>
## TNT 系列 <sup>[[35](#ref35)]</sup>
关于 TNT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[TNT 系列模型文档](TNT.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
add models
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| TNT_small | 0.8148 |0.9580 | | | 4.83 | 23.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/TNT_small_infer.tar) |
| TNT_base | 0.8276 |0.9617 | | | 13.40 | 65.30 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/TNT_base_infer.tar) |
docs: fix invalid links
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**注**：TNT 模型的数据预处理部分 `NormalizeImage` 中的 `mean` 与 `std` 均为 0.5。
Add NextViT code and docs
2022-11-22 16:48:40 +08:00
<a name="NextViT"></a>
modify latency
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add uniformer lst
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## NextViT 系列 <sup>[[47](#ref47)]</sup>
Add NextViT code and docs
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关于 NextViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[NextViT 系列模型文档](NextViT.md)。
update README.md
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
update latency
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| NextViT_small_224 | 0.8248 | 0.9616 | 7.76 | 10.86 | 14.20 | 5.79 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_224_infer.tar) |
| NextViT_base_224 | 0.8324 | 0.9658 | 12.01 | 16.21 | 20.63 | 8.26 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_224_infer.tar) |
| NextViT_large_224 | 0.8363 | 0.9661 | 16.51 | 21.91 | 27.25 | 10.73 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_224_infer.tar) |
update README.md
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| NextViT_small_384 | 0.8401 | 0.9698 | - | - | - | 17.00 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_384_infer.tar) |
| NextViT_base_384 | 0.8465 | 0.9723 | - | - | - | 24.27 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_384_infer.tar) |
| NextViT_large_384 | 0.8492 | 0.9728 | - | - | - | 31.53 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_384_infer.tar) |
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| NextViT_small_224_ssld | 0.8472 | 0.9734 | 7.76 | 10.86 | 14.20 | 5.79 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_224_ssld_infer.tar) |
| NextViT_base_224_ssld | 0.8500 | 0.9753 | 12.01 | 16.21 | 20.63 | 8.26 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_224_ssld_infer.tar) |
| NextViT_large_224_ssld | 0.8536 | 0.9762 | 16.51 | 21.91 | 27.25 | 10.73 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_224_ssld_infer.tar) |
update README.md
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| NextViT_small_384_ssld | 0.8597 | 0.9790 | - | - | - | 17.00 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_384_ssld_infer.tar) |
| NextViT_base_384_ssld | 0.8634 | 0.9806 | - | - | - | 24.27 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_384_ssld_infer.tar) |
| NextViT_large_384_ssld | 0.8654 | 0.9814 | - | - | - | 31.53 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_384_ssld_infer.tar) |
Add NextViT code and docs
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add uniformer lst
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<a name="UniFormer"></a>
## UniFormer 系列 <sup>[[48](#ref48)]</sup>
关于 UniFormer 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[UniFomer 系列模型文档](UniFormer.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
update latency
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| UniFormer_small | 0.8294 | 0.9631 | 3.68 | 5.93 | 9.64 | 3.44 | 21.55 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_small_infer.tar) |
| UniFormer_small_plus | 0.8329 | 0.9656 | 4.12 | 7.03 | 11.59 | 3.99 | 24.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_small_plus_infer.tar) |
| UniFormer_small_plus_dim64 | 0.8325 | 0.9649 | 3.91 | 6.56 | 10.69 | 3.99 | 24.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_dim64_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_small_plus_dim64_infer.tar) |
| UniFormer_base | 0.8376 | 0.9672 | 8.19 | 12.98 |21.29 | 7.77 | 49.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_base_infer.tar) |
| UniFormer_base_ls | 0.8398 | 0.9675 | 14.79 | - | 22.20 | 7.77 | 49.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_ls_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_base_ls_infer.tar) |
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add docs, config, model for DSNet
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<a name="DSNet"></a>
## DSNet 系列 <sup>[[49](#ref49)]</sup>
关于 DSNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[DSNet 系列模型文档](DSNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ----------- | --------- | --------- | ---------------- | ---------------- | ----------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| DSNet_tiny | 0.7919 | 0.9476 | - | - | - | 1.8 | 10.5 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DSNet_tiny_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DSNet_tiny_infer.tar) |
modified DSNet docs
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| DSNet_small | 0.8196 | 0.9596 | - | - | - | 3.5 | 23.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DSNet_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DSNet_small_infer.tar) |
add docs, config, model for DSNet
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| DSNet_base | 0.8175 | 0.9522 | - | - | - | 8.4 | 49.3 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DSNet_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DSNet_base_infer.tar) |
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docs: fix invalid links
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<a name="Transformer_lite"></a>
### 4.2 轻量级模型
<a name="MobileViT"></a>
rename v2 to V2
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## MobileViT 系列 <sup>[[42](#ref42)][[51](#ref51)][[52](#ref52)]</sup>
docs: fix invalid links
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关于 MobileViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MobileViT 系列模型文档](MobileViT.md)，[MobileViTV2 系列模型文档](MobileViTV2.md)，[MobileViTv3 系列模型文档](MobileViTv3.md)。
docs: fix invalid links
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
update
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| MobileViT_XXS | 0.6867 | 0.8878 | - | - | - | 337.24 | 1.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
| MobileViT_XS | 0.7454 | 0.9227 | - | - | - | 930.75 | 2.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XS_infer.tar) |
| MobileViT_S | 0.7814 | 0.9413 | - | - | - | 1849.35 | 5.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
rename v2 to V2
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| MobileViTV2_x0_5 | 0.7017 | 0.8988 | - | - | - | 480.46 | 1.37 | [下载链接]() | [下载链接]() |
| MobileViTV2_x1_0 | 0.7813 | 0.9417 | - | - | - | 1843.81 | 4.90 | [下载链接]() | [下载链接]() |
| MobileViTV2_x1_5 | 0.8034 | 0.9509 | - | - | - | 4090.07 | 10.60 | [下载链接]() | [下载链接]() |
| MobileViTV2_x2_0 | 0.8116 | 0.9537 | - | - | - | 7219.23 | 18.45 | [下载链接]() | [下载链接]() |
update
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| MobileViTv3_XXS | 0.7087 | 0.8976 | - | - | - | 289.02 | 1.25 | [下载链接]() | [下载链接]() |
| MobileViTv3_XS | 0.7663 | 0.9332 | - | - | - | 926.98 | 2.49 | [下载链接]() | [下载链接]() |
| MobileViTv3_S | 0.7928 | 0.9454 | - | - | - | 1841.39 | 5.76 | [下载链接]() | [下载链接]() |
| MobileViTv3_XXS_L2 | 0.7028 | 0.8942 | - | - | - | 256.97 | 1.15 | [下载链接]() | [下载链接]() |
| MobileViTv3_XS_L2 | 0.7607 | 0.9300 | - | - | - | 852.82 | 2.26 | [下载链接]() | [下载链接]() |
| MobileViTv3_S_L2 | 0.7907 | 0.9440 | - | - | - | 1651.96 | 5.17 | [下载链接]() | [下载链接]() |
| MobileViTv3_x0_5 | 0.7200 | 0.9083 | - | - | - | 481.33 | 1.43 | [下载链接]() | [下载链接]() |
| MobileViTv3_x0_75 | 0.7626 | 0.9308 | - | - | - | 1064.48 | 3.00 | [下载链接]() | [下载链接]() |
| MobileViTv3_x1_0 | 0.7838 | 0.9421 | - | - | - | 1875.96 | 5.14 | [下载链接]() | [下载链接]() |
docs: fix invalid links
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<a name='reference'></a>
## 五、参考文献
<a name="ref1">[1]</a> He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
<a name="ref2">[2]</a> He T, Zhang Z, Zhang H, et al. Bag of tricks for image classification with convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 558-567.
<a name="ref3">[3]</a> Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1314-1324.
<a name="ref4">[4]</a> Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.
<a name="ref5">[5]</a> Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017.
<a name="ref6">[6]</a> Ma N, Zhang X, Zheng H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 116-131.
<a name="ref7">[7]</a> Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1492-1500.
<a name="ref8">[8]</a> Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.
<a name="ref9">[9]</a> Gao S, Cheng M M, Zhao K, et al. Res2net: A new multi-scale backbone architecture[J]. IEEE transactions on pattern analysis and machine intelligence, 2019.
<a name="ref10">[10]</a> Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.
<a name="ref11">[11]</a> Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Thirty-first AAAI conference on artificial intelligence. 2017.
<a name="ref12">[12]</a> Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.
<a name="ref13">[13]</a> Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition[J]. arXiv preprint arXiv:1908.07919, 2019.
<a name="ref14">[14]</a> Chen Y, Li J, Xiao H, et al. Dual path networks[C]//Advances in neural information processing systems. 2017: 4467-4475.
<a name="ref15">[15]</a> Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708.
<a name="ref16">[16]</a> Tan M, Le Q V. Efficientnet: Rethinking model scaling for convolutional neural networks[J]. arXiv preprint arXiv:1905.11946, 2019.
<a name="ref17">[17]</a> Mahajan D, Girshick R, Ramanathan V, et al. Exploring the limits of weakly supervised pretraining[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 181-196.
<a name="ref18">[18]</a> Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.
<a name="ref19">[19]</a> Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J]. arXiv preprint arXiv:1602.07360, 2016.
<a name="ref20">[20]</a> Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
<a name="ref21">[21]</a> Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
<a name="ref22">[22]</a> Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920.
<a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.
<a name="ref24">[24]</a> Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020.
<a name="ref25">[25]</a> Radosavovic I, Kosaraju R P, Girshick R, et al. Designing network design spaces[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 10428-10436.
<a name="ref26">[26]</a> C.Szegedy, V.Vanhoucke, S.Ioffe, J.Shlens, and Z.Wojna. Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567, 2015.
<a name="ref27">[27]</a> Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.
<a name="ref28">[28]</a>Cheng Cui, Tingquan Gao, Shengyu Wei, Yuning Du, Ruoyu Guo, Shuilong Dong, Bin Lu, Ying Zhou, Xueying Lv, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma. PP-LCNet: A Lightweight CPU Convolutional Neural Network.
<a name="ref29">[29]</a>Mingxing Tan, Quoc V. Le. MixConv: Mixed Depthwise Convolutional Kernels.
<a name="ref30">[30]</a>Dongyoon Han, Sangdoo Yun, Byeongho Heo, YoungJoon Yoo. Rethinking Channel Dimensions for Efficient Model Design.
<a name="ref31">[31]</a>Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. AN IMAGE IS WORTH 16X16 WORDS:
TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE.
<a name="ref32">[32]</a>Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Herve Jegou. Training data-efficient image transformers & distillation through attention.
<a name="ref33">[33]</a>Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Herve Jegou, Matthijs Douze. LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference.
<a name="ref34">[34]</a>Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen. Twins: Revisiting the Design of Spatial Attention in Vision Transformers.
<a name="ref35">[35]</a>Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang. Transformer in Transformer.
<a name="ref36">[36]</a>Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun. RepVGG: Making VGG-style ConvNets Great Again.
<a name="ref37">[37]</a>Ping Chao, Chao-Yang Kao, Yu-Shan Ruan, Chien-Hsiang Huang, Youn-Long Lin. HarDNet: A Low Memory Traffic Network.
<a name="ref38">[38]</a>Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell. Deep Layer Aggregation.
<a name="ref39">[39]</a>Duo Lim Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen. Involution: Inverting the Inherence of Convolution for Visual Recognition.
<a name="ref40">[40]</a>Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, Baining Guo. CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows.
<a name="ref41">[41]</a>Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. PVTv2: Improved Baselines with Pyramid Vision Transformer.
<a name="ref42">[42]</a>Sachin Mehta, Mohammad Rastegari. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.
<a name="ref43">[43]</a>Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. A ConvNet for the 2020s.
<a name="ref44">[44]</a>Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu. Visual Attention Network.
<a name="ref45">[45]</a>Robert J. Wang, Xiang Li, Charles X. Ling. Pelee: A Real-Time Object Detection System on Mobile Devices
<a name="ref46">[46]</a>Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh. CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Add NextViT code and docs
2022-11-22 16:48:40 +08:00
add uniformer lst
2022-12-02 18:16:23 +08:00
<a name="ref47">[47]</a>Jiashi Li, Xin Xia, Wei Li, Huixia Li, Xing Wang, Xuefeng Xiao, Rui Wang, Min Zheng, Xin Pan. Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios.
<a name="ref48">[48]</a>Kunchang Li, Yali Wang, Junhao Zhang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, Yu Qiao. UniFormer: Unifying Convolution and Self-attention for Visual Recognition
add docs, config, model for DSNet
2022-12-14 14:35:19 +08:00
<a name="ref49">[49]</a>Mingyuan Mao, Renrui Zhang, Honghui Zheng, Peng Gao, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han. Dual-stream Network for Visual Recognition.
add docs
2023-01-04 11:50:22 +08:00
<a name="ref50">[50]</a>Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. Swin Transformer V2: Scaling Up Capacity and Resolution
update
2023-03-03 03:30:20 +08:00
add MobileViTv2
2023-04-17 16:02:28 +08:00
<a name="ref51">[51]</a>Sachin Mehta and Mohammad Rastegari. Separable Self-attention for Mobile Vision Transformers
rename v2 to V2
2023-04-17 16:39:18 +08:00
<a name="ref52">[52]</a>Wadekar, Shakti N. and Chaurasia, Abhishek. MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features