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docs/docs/model_zoo.md
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| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型大小（MB） | 下载 |
|:--:|:---:|:--:|:--:|:--:|
|MobileNetV1|-|70.99%/89.68%| xx | [下载链接]() |
|MobileNetV1|ResNet50_vd<sup>[1](#trans1)</sup> distill|xx%/xx%| xx | [下载链接]() |
| MobileNetV2 | - |72.15%/90.65%| xx | [下载链接]() |
| MobileNetV2 | ResNet50_vd<sup>[1](#trans1)</sup> distill |xx%/xx%| xx | [下载链接]() |
|ResNet50|-|76.50%/93.00%| xx | [下载链接]() |
|ResNet50|ResNet101<sup>[2](#trans2)</sup> distill|xx%/xx%| xx | [下载链接]() |
| MobileNetV1 | - | 70.99%/89.68% | 17 | [下载链接]() |
|ResNet50_vd|-|79.12%/94.44%| 99 | [下载链接]() |
|MobileNetV1|ResNet50_vd<sup>[1](#trans1)</sup> distill|72.77%/90.68%| 17 | [下载链接]() |
| MobileNetV2 | - | 72.15%/90.65% | 15 | [下载链接]() |
| MobileNetV2 | ResNet50_vd distill | 74.28%/91.53% | 15 | [下载链接]() |
| ResNet50 | - | 76.50%/93.00% | 99 | [下载链接]() |
|ResNet101|-|77.56%/93.64%| 173 | [下载链接]() |
| ResNet50 | ResNet101 distill | 77.29%/93.65% | 99 | [下载链接]() |
!!! note "Note"
<a name="trans1">[1]</a>：[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar)预训练模型Top-1/Top-5准确率分别为79.12%/94.44%
带_vd后缀代表开启了Mixup训练，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
<a name="trans2">[2]</a>：[ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar)预训练模型Top-1/Top-5准确率分别为77.56%/93.64%
<a name="trans1">[1]</a>：带_vd后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
## 2. 目标检测
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数据集：Pasacl VOC & COCO 2017
| 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型大小（MB） | 下载 |
| :-----------------: | :--------------------------------------------: | :--------: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |
| MobileNet-V1-YOLOv3 | - | Pasacl VOC | 8 | 76.2 | 76.7 | 75.3 | xx | [下载链接]() |
| MobileNet-V1-YOLOv3 | ResNet34-YOLOv3<sup>[3](#trans3)</sup> distill | Pasacl VOC | 8 | xx | xx | xx | xx | [下载链接]() |
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | xx | [下载链接]() |
| MobileNet-V1-YOLOv3 | ResNet34-YOLOv3<sup>[4](#trans4)</sup> distill | COCO | 8 | xx | xx | xx | xx | [下载链接]() |
!!! note "Note"
<a name="trans3">[3]</a>：[ResNet34-YOLOv3-VOC]()预训练模型在608/416/320尺寸输入下的Box AP分别为82.6/81.9/80.1
| 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型大小（MB） | 下载 |
| :-----------------: | :---------------------: | :--------: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |
| MobileNet-V1-YOLOv3 | - | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | [下载链接]() |
| ResNet34-YOLOv3 | - | Pascal VOC | 8 | 82.6 | 81.9 | 80.1 | 162 | [下载链接]() |
| MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill | Pascal VOC | 8 | 79.0 | 78.2 | 75.5 | 94 | [下载链接]() |
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.0 | 95 | [下载链接]() |
| ResNet34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 163 | [下载链接]() |
| MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill | COCO | 8 | 31.4 | 30.0 | 27.1 | 95 | [下载链接]() |
<a name="trans4">[4]</a>：[ResNet34-YOLOv3-COCO]()预训练模型在608/416/320尺寸输入下的Box AP分别为36.2/34.3/31.4
## 3. 图像分割
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