model_zoo.md

# PaddleSeg 预训练模型

PaddleSeg对所有内置的分割模型都提供了公开数据集下的预训练模型。因为对于自定
义数据集的场景，使用预训练模型进行训练可以得到更稳定地效果。用户可以根据模型类型、自己的数据集和预训练数据集的相似程度，选择并下载预训练模型。

## ImageNet预训练模型

所有Imagenet预训练模型来自于PaddlePaddle图像分类库，想获取更多细节请点击[这里](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification)

| 模型 | 数据集合 | Depth multiplier | 下载地址 | Accuray Top1/5 Error|
|---|---|---|---|---|
| MobileNetV2_1.0x  | ImageNet | 1.0x | [MobileNetV2_1.0x](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | 72.15%/90.65% |
| MobileNetV2_0.25x | ImageNet | 0.25x |[MobileNetV2_0.25x](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_25_pretrained.tar) | 53.21%/76.52% |
| MobileNetV2_0.5x  | ImageNet | 0.5x | [MobileNetV2_0.5x](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_5_pretrained.tar) | 65.03%/85.72% |
| MobileNetV2_1.5x  | ImageNet | 1.5x | [MobileNetV2_1.5x](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x1_5_pretrained.tar) | 74.12%/91.67% |
| MobileNetV2_2.0x  | ImageNet | 2.0x | [MobileNetV2_2.0x](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x2_0_pretrained.tar) | 75.23%/92.58% |
| MobileNetV3_Large_ssld_1.0x  | ImageNet | 1.0x | [MobileNetV3_Large_ssld_1.0x](https://paddleseg.bj.bcebos.com/models/MobileNetV3_large_x1_0_ssld_pretrained.tar) | 79.00%/94.50% |

用户可以结合实际场景的精度和预测性能要求，选取不同`Depth multiplier`参数的MobileNet模型。

| 模型 | 数据集合 | 下载地址 | Accuray Top1/5 Error |
|---|---|---|---|
| Xception41 | ImageNet | [Xception41_pretrained.tgz](https://paddleseg.bj.bcebos.com/models/Xception41_pretrained.tgz) | 79.5%/94.38% |
| Xception65 | ImageNet | [Xception65_pretrained.tgz](https://paddleseg.bj.bcebos.com/models/Xception65_pretrained.tgz) | 80.32%/94.47% |
| Xception71 | ImageNet | coming soon | -- |

| 模型 | 数据集合 | 下载地址 | Accuray Top1/5 Error |
|---|---|---|---|
| HRNet_W18 | ImageNet | [hrnet_w18_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w18_imagenet.tar) | 76.92%/93.39% |
| HRNet_W30 | ImageNet | [hrnet_w30_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w30_imagenet.tar) | 78.04%/94.02% |
| HRNet_W32 | ImageNet | [hrnet_w32_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w32_imagenet.tar) | 78.28%/94.24% |
| HRNet_W40 | ImageNet | [hrnet_w40_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w40_imagenet.tar) | 78.77%/94.47% |
| HRNet_W44 | ImageNet | [hrnet_w44_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w44_imagenet.tar) | 79.00%/94.51% |
| HRNet_W48 | ImageNet | [hrnet_w48_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w48_imagenet.tar) | 78.95%/94.42% |
| HRNet_W64 | ImageNet | [hrnet_w64_imagenet.tar](https://paddleseg.bj.bcebos.com/models/hrnet_w64_imagenet.tar) | 79.30%/94.61% |

| 模型 | 数据集合 | 下载地址 | Accuray Top1/5 Error |
|---|---|---|---|
| ResNet50（适配PSPNet） | ImageNet | [resnet50_v2_pspnet](https://paddleseg.bj.bcebos.com/resnet50_v2_pspnet.tgz)| -- |
| ResNet101（适配PSPNet） | ImageNet | [resnet101_v2_pspnet](https://paddleseg.bj.bcebos.com/resnet101_v2_pspnet.tgz)| -- |
| ResNet50_vd | ImageNet | [ResNet50_vd_ssld_pretrained.tgz](https://paddleseg.bj.bcebos.com/models/ResNet50_vd_ssld_pretrained.tgz) | 83.0%/96.4% |

## COCO预训练模型

数据集为COCO实例分割数据集合转换成的语义分割数据集合

| 模型 | 数据集合 | 下载地址 |Output Strid|multi-scale test| mIoU |
|---|---|---|---|---|---|
| DeepLabv3+/MobileNetv2/bn | COCO |[deeplab_mobilenet_x1_0_coco.tgz](https://bj.bcebos.com/v1/paddleseg/deeplab_mobilenet_x1_0_coco.tgz) | 16 | --| -- |
| DeeplabV3+/Xception65/bn | COCO | [xception65_coco.tgz](https://paddleseg.bj.bcebos.com/models/xception65_coco.tgz)| 16 | -- | -- |
| U-Net/bn | COCO | [unet_coco.tgz](https://paddleseg.bj.bcebos.com/models/unet_coco_v3.tgz) | 16 | -- | -- |
| PSPNet/bn | COCO | [pspnet50_coco.tgz](https://paddleseg.bj.bcebos.com/models/pspnet50_coco.tgz) | 16 | -- | -- |
| PSPNet/bn | COCO | [pspnet101_coco.tgz](https://paddleseg.bj.bcebos.com/models/pspnet101_coco.tgz) | 16 | -- | -- |

## Cityscapes预训练模型

train数据集合为Cityscapes训练集合，测试为Cityscapes的验证集合

| 模型 | 数据集合 | 下载地址 |Output Stride| mutli-scale test| mIoU on val|
|---|---|---|---|---|---|
| DeepLabv3+/MobileNetv2/bn | Cityscapes |[mobilenet_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz) |16|false| 0.698|
| DeepLabv3+/MobileNetv3_Large/bn | Cityscapes |[deeplabv3p_mobilenetv3_large_cityscapes.tar.gz](https://paddleseg.bj.bcebos.com/models/deeplabv3p_mobilenetv3_large_cityscapes.tar.gz) |32|false| 0.7328|
| DeepLabv3+/Xception65/gn  | Cityscapes |[deeplabv3p_xception65_gn_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/deeplabv3p_xception65_cityscapes.tgz) |16|false| 0.7824 |
| DeepLabv3+/Xception65/bn | Cityscapes |[deeplabv3p_xception65_bn_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/xception65_bn_cityscapes.tgz) | 16 | false | 0.7930 |
| DeepLabv3+/ResNet50_vd/bn | Cityscapes |[deeplabv3p_resnet50_vd_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/deeplabv3p_resnet50_vd_cityscapes.tgz) | 16 | false | 0.8006 |
| ICNet/bn | Cityscapes |[icnet_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/icnet_cityscapes.tar.gz) |16|false| 0.6831 |
| PSPNet/bn | Cityscapes |[pspnet50_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/pspnet50_cityscapes.tgz) |16|false| 0.7013 |
| PSPNet/bn | Cityscapes |[pspnet101_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/pspnet101_cityscapes.tgz) |16|false| 0.7734 |
| HRNet_W18/bn | Cityscapes |[hrnet_w18_bn_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/hrnet_w18_bn_cityscapes.tgz) | 4 | false | 0.7936 |
| Fast-SCNN/bn | Cityscapes |[fast_scnn_cityscapes.tar](https://paddleseg.bj.bcebos.com/models/fast_scnn_cityscape.tar) | 32 | false | 0.6964 |
| OCRNet/bn | Cityscapes |[ocrnet_w18_bn_cityscapes.tar.gz](https://paddleseg.bj.bcebos.com/models/ocrnet_w18_bn_cityscapes.tar.gz) | 4 | false | 0.8023 |

测试环境为python 3.7.3，v100，cudnn 7.6.2。