## 1. Training Benchmark### 1.1 Environment* The training process of PP-Matting model uses one GPU and batch size 4.### 1.2 Datasets* The common object matting dataset is Compositon-1k or Distinctins-646 (the use of both datasets are requested from the author). COCO2017 and Pascal VOC 2012 are used as the background datasets.* Human matting uses the private dataset.### 1.3 Benchmark|Model | Description | Input shape ||---|---|---||ppmatting_hrnet_w48 | Common object matting | 512 ||ppmatting_hrnet_w18 | Human matting | 512 |## 2. Inference Benchmark### 2.1 Environment* The PP-Matting model's inference speed test is tested with one V100, batch size=1, CUDA 10.2, CUDNN 7.6.5, PaddlePaddle-gpu 2.3.2.### 2.2 Datasets* Common object matting: the test dataset of Compositon-1k or Distinctions-646.* Human matting: the human image of PPM-100 and AIM-500, total 195 images, named PPM-AIM-195.### 2.3 Benchmark| Model | Dataset | SAD | MSE | Grad | Conn |Params(M) | FLOPs(G) | FPS || - | - | -| - | - | - | - | -| - || ppmatting_hrnet_w48 | Composition-1k | 46.22 | 0.005 | 22.69 | 45.40 | 86.3 | 165.4 | 24.4 || ppmatting_hrnet_w48 | Distinctions-646 | 40.69 | 0.009 | 43.91 |40.56 | 86.3 | 165.4 | 24.4 || ppmatting_hrnet_w18 | PPM-AIM-195 | 31.56|0.0022|31.80|30.13| 24.5 | 91.28 | 28.9 |## 3. Reference1. https://github.com/PaddlePaddle/PaddleSeg/tree/develop/Matting
