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未验证 提交 99f52dcc 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #144 from SunAhong1993/syf0609 

modify the model zoo benchmark
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docs/appendix/model_zoo.md
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| 模型 | 模型大小 | 预测速度（毫秒） | Top1准确率（%） | Top5准确率（%） |
| :----| :------- | :----------- | :--------- | :--------- |
| ResNet18| 46.9MB | - | 71.0 | 89.9 |
| ResNet34| 87.5MB | - | 74.6 | 92.1 |
| ResNet50| 102.7MB | - | 76.5 | 93.0 |
| ResNet101 |179.1MB | - | 77.6 | 93.6 |
| ResNet50_vd |102.8MB |- | 79.1 | 94.4 |
| ResNet101_vd| 179.2MB | - | 80.2 | 95.0 |
| ResNet50_vd_ssld |102.8MB | - | 82.4 | 96.1 |
| ResNet101_vd_ssld| 179.2MB | - | 83.7 | 96.7 |
| DarkNet53|166.9MB | - | 78.0 | 94.1 |
| MobileNetV1 | 16.0MB | - | 71.0 | 89.7 |
| MobileNetV2 | 14.0MB | - | 72.2 | 90.7 |
| MobileNetV3_large| 21.0MB | - | 75.3 | 93.2 |
| MobileNetV3_small | 12.0MB | - | 68.2 | 88.1 |
| MobileNetV3_large_ssld| 21.0MB | - | 79.0 | 94.5 |
| MobileNetV3_small_ssld | 12.0MB | - | 71.3 | 90.1 |
| Xception41 |92.4MB | - | 79.6 | 94.4 |
| Xception65 | 144.6MB | - | 80.3 | 94.5 |
| DenseNet121 | 32.8MB | - | 75.7 | 92.6 |
| DenseNet161|116.3MB | - | 78.6 | 94.1 |
| DenseNet201| 84.6MB | - | 77.6 | 93.7 |
| ShuffleNetV2 | 9.0MB | - | 68.8 | 88.5 |
| HRNet_W18 | 21.29MB | - | 76.9 | 93.4 |
| [ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar)| 46.2MB | 3.72882 | 71.0 | 89.9 |
| [ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar)| 87.9MB | 5.50876 | 74.6 | 92.1 |
| [ResNet50](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar)| 103.4MB | 7.76659 | 76.5 | 93.0 |
| [ResNet101](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) |180.4MB | 13.80876 | 77.6 | 93.6 |
| [ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) |103.5MB | 8.20476 | 79.1 | 94.4 |
| [ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar)| 180.5MB | 14.24643 | 80.2 | 95.0 |
| [ResNet50_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar) |103.5MB | 7.79264 | 82.4 | 96.1 |
| [ResNet101_vd_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_ssld_pretrained.tar)| 180.5MB | 13.34580 | 83.7 | 96.7 |
| [DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar)|167.4MB | 8.82047 | 78.0 | 94.1 |
| [MobileNetV1](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | 17.4MB | 3.42838 | 71.0 | 89.7 |
| [MobileNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | 15.0MB | 5.92667 | 72.2 | 90.7 |
| [MobileNetV3_large](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar)| 22.8MB | 8.31428 | 75.3 | 93.2 |
| [MobileNetV3_small](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_pretrained.tar) | 12.5MB | 7.30689 | 68.2 | 88.1 |
| [MobileNetV3_large_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar)| 22.8MB | 8.06651 | 79.0 | 94.5 |
| [MobileNetV3_small_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar) | 12.5MB | 7.08837 | 71.3 | 90.1 |
| [Xception41](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_deeplab_pretrained.tar) | 109.2MB | 8.15611 | 79.6 | 94.4 |
| [Xception65](https://paddle-imagenet-models-name.bj.bcebos.com/Xception65_deeplab_pretrained.tar) | 161.6MB | 13.87017 | 80.3 | 94.5 |
| [DenseNet121](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet121_pretrained.tar) | 33.1MB | 17.09874 | 75.7 | 92.6 |
| [DenseNet161](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet161_pretrained.tar)| 118.0MB | 22.79690 | 78.6 | 94.1 |
| [DenseNet201](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet201_pretrained.tar)| 84.1MB | 25.26089 | 77.6 | 93.7 |
| [ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 10.2MB | 15.40138 | 68.8 | 88.5 |
| [HRNet_W18](https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar) | 21.29MB |45.25514 | 76.9 | 93.4 |
## 目标检测模型
> 表中模型相关指标均为在MSCOCO数据集上使用PaddlePaddle Python预测接口测试得到（测试GPU型号为Nvidia Tesla V100测试得到,表中符号`-`表示相关指标暂未测试。
> 表中模型相关指标均为在MSCOCO数据集上使用PaddlePaddle Python预测接口测试得到（测试GPU型号为Nvidia Tesla V100测试得到）,表中符号`-`表示相关指标暂未测试。
| 模型 | 模型大小 | 预测时间(毫秒) | BoxAP（%） |
|:-------|:-----------|:-------------|:----------|
|FasterRCNN-ResNet50|135.6MB| 78.450 | 35.2 |
|FasterRCNN-ResNet50_vd| 135.7MB | 79.523 | 36.4 |
|FasterRCNN-ResNet101| 211.7MB | 107.342 | 38.3 |
|FasterRCNN-ResNet50-FPN| 167.2MB | 44.897 | 37.2 |
|FasterRCNN-ResNet50_vd-FPN|168.7MB | 45.773 | 38.9 |
|FasterRCNN-ResNet101-FPN| 251.7MB | 55.782 | 38.7 |
|FasterRCNN-ResNet101_vd-FPN |252MB | 58.785 | 40.5 |
|FasterRCNN-HRNet_W18-FPN |115.5MB | 57.11 | 36 |
|YOLOv3-DarkNet53|252.4MB | 21.944 | 38.9 |
|YOLOv3-MobileNetv1 |101.2MB | 12.771 | 29.3 |
|YOLOv3-MobileNetv3|94.6MB | - | 31.6 |
| YOLOv3-ResNet34|169.7MB | 15.784 | 36.2 |
|[FasterRCNN-ResNet50](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar)|136.0MB| 197.715 | 35.2 |
|[FasterRCNN-ResNet50_vd](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar)| 136.1MB | 475.700 | 36.4 |
|[FasterRCNN-ResNet101](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar)| 212.5MB | 582.911 | 38.3 |
|[FasterRCNN-ResNet50-FPN](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar)| 167.7MB | 83.189 | 37.2 |
|[FasterRCNN-ResNet50_vd-FPN](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar)|167.8MB | 128.277 | 38.9 |
|[FasterRCNN-ResNet101-FPN](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar)| 244.2MB | 156.097 | 38.7 |
|[FasterRCNN-ResNet101_vd-FPN](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar) |244.3MB | 119.788 | 40.5 |
|[FasterRCNN-HRNet_W18-FPN](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_1x.tar) |115.5MB | 81.592 | 36 |
|[YOLOv3-DarkNet53](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar)|249.2MB | 42.672 | 38.9 |
|[YOLOv3-MobileNetV1](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |99.2MB | 15.442 | 29.3 |
|[YOLOv3-MobileNetV3_large](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams)|100.7MB | 143.322 | 31.6 |
| [YOLOv3-ResNet34](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar)|170.3MB | 23.185 | 36.2 |
## 实例分割模型
> 表中模型相关指标均为在MSCOCO数据集上测试得到。
| 模型 | 模型大小 | 预测时间(毫秒) | mIoU（%） |
|:-------|:-----------|:-------------|:----------|
|DeepLabv3+-MobileNetV2_x1.0|-| - | - |
|DeepLabv3+-Xception41|-| - | - |
|DeepLabv3+-Xception65|-| - | - |
|UNet|-| - | - |
|HRNet_w18|-| - | - |
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