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@@ -6,6 +6,8 @@
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- [通用目标抠图](https://paddleseg.bj.bcebos.com/matting/models/deploy/ppmatting-hrnet_w48-composition.zip)
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- [人物抠图](https://paddleseg.bj.bcebos.com/matting/models/ppmattingv2-stdc1-human_512.pdparams)
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+人物抠图效果比较好，其它的没有尝试
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## 目录
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* [简介](#简介)
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* [更新动态](#更新动态)
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@@ -65,8 +67,9 @@ Image Matting（精细化分割/影像去背/抠图）是指借由计算前景
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- PP-MattingV2是PaddleSeg自研的轻量级抠图SOTA模型，通过双层金字塔池化及空间注意力提取高级语义信息，并利用多级特征融合机制兼顾语义和细节的预测。
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对比MODNet模型推理速度提升44.6%， 误差平均相对减小17.91%。追求更高速度，推荐使用该模型。
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| 模型 | SAD | MSE | Grad | Conn |Params(M) | FLOPs(G) | FPS | Config File | Checkpoint | Inference Model |
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-| - | - | -| - | - | - | - | -| - | - | - |
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+| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
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| PP-MattingV2-512 |40.59|0.0038|33.86|38.90| 8.95 | 7.51 | 98.89 |[cfg](../configs/ppmattingv2/ppmattingv2-stdc1-human_512.yml)| [model](https://paddleseg.bj.bcebos.com/matting/models/ppmattingv2-stdc1-human_512.pdparams) | [model inference](https://paddleseg.bj.bcebos.com/matting/models/deploy/ppmattingv2-stdc1-human_512.zip) |
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| PP-Matting-512 |31.56|0.0022|31.80|30.13| 24.5 | 91.28 | 28.9 |[cfg](../configs/ppmatting/ppmatting-hrnet_w18-human_512.yml)| [model](https://paddleseg.bj.bcebos.com/matting/models/ppmatting-hrnet_w18-human_512.pdparams) | [model inference](https://paddleseg.bj.bcebos.com/matting/models/deploy/ppmatting-hrnet_w18-human_512.zip) |
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| PP-Matting-1024 |66.22|0.0088|32.90|64.80| 24.5 | 91.28 | 13.4(1024X1024) |[cfg](../configs/ppmatting/ppmatting-hrnet_w18-human_1024.yml)| [model](https://paddleseg.bj.bcebos.com/matting/models/ppmatting-hrnet_w18-human_1024.pdparams) | [model inference](https://paddleseg.bj.bcebos.com/matting/models/deploy/ppmatting-hrnet_w18-human_1024.zip) |
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@@ -76,6 +79,7 @@ Image Matting（精细化分割/影像去背/抠图）是指借由计算前景
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| MODNet-HRNet_W18 |35.55|0.0035|31.73|34.07| 10.2 | 28.5 | 62.6 |[cfg](../configs/modnet/modnet-hrnet_w18.yml)| [model](https://paddleseg.bj.bcebos.com/matting/models/modnet-hrnet_w18.pdparams) | [model inference](https://paddleseg.bj.bcebos.com/matting/models/deploy/modnet-hrnet_w18.zip) |
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| DIM-VGG16 |32.31|0.0233|28.89|31.45| 28.4 | 175.5| 30.4 |[cfg](../configs/dim/dim-vgg16.yml)| [model](https://paddleseg.bj.bcebos.com/matting/models/dim-vgg16.pdparams) | [model inference](https://paddleseg.bj.bcebos.com/matting/models/deploy/dim-vgg16.zip) |
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**注意**：
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* 指标计算数据集为PPM-100和AIM-500中的人像部分共同组成，共195张，[PPM-AIM-195](https://paddleseg.bj.bcebos.com/matting/datasets/PPM-AIM-195.zip)。
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* FLOPs和FPS计算默认模型输入大小为(512, 512), GPU为Tesla V100 32G。FPS基于Paddle Inference预测库进行计算。
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