采用公开数据集 Helen
包含 11类标签： hair,eyebrows, eyes, nose, lips, in mouth, skin and background.
包含 194个人脸关键点，training: 2000 val: 330
将训练集做随机反转、旋转、剪切进行数据扩充，training: 10146 val: 330
以UNet为基础网络框架，将编码器部分改为resnet18和mobilenet ，使用在imagenet上的预训练模型，在解码器部分添加attention gate，训练准确率有提升。
overall acc表示11个类别的平均准确率
| Model | Overall acc(Tr) | Overall acc(val) | mIOU (tr) | mIOU (val) | Params (M) |
|---|---|---|---|---|---|
| UNet | 0.9292 | 0.9033 | 0.6142 | 0.5315 | 118.49 |
| Resnet18 | 0.9679 | 0.9274 | 0.7885 | 0.6123 | 69.94 |
| Resnet18+ImN | 0.9697 | 0.9296 | 0.8016 | 0.6402 | 69.94 |
| mobile | 0.9466 | 0.9248 | 0.6787 | 0.6177 | 17.17 |
| mobile+ImN | 0.9689 | 0.9334 | 0.7976 | 0.6969 | 17.17 |
ImN: initializing the network from the weights pretrained on ImageNet dataset.
UNet (mobilenet encoder + ImN) per class acc
| classname | background | face | left eyebrow | right eyebrow | left eye | right eye | nose | upper lip | inner mouth | lower lip | hair |
|---|---|---|---|---|---|---|---|---|---|---|---|
| acc | 0.962 | 0.932 | 0.727 | 0.737 | 0.800 | 0.783 | 0.935 | 0.751 | 0.803 | 0.812 | 0.790 |
| miou | 0.930 | 0.875 | 0.570 | 0.559 | 0.589 | 0.615 | 0.849 | 0.596 | 0.598 | 0.675 | 0.663 |
