Official repository for the paper Rethinking Context Aggregation in Natural Image Matting
AEMatter is a simple yet powerful matting network.
GPU memory >= 10GB for inference on Adobe Composition-1K testing set.
- torch >= 1.10
- numpy >= 1.16
- opencv-python >= 4.0
- einops >= 0.3.2
- timm >= 0.4.12
The model can only be used and distributed for noncommercial purposes.
Quantitative results on Adobe Composition-1K
| Model Name | Size | MSE | SAD | Grad | Conn |
|---|---|---|---|---|---|
| AEMatter | 195MiB | 2.26 | 17.53 | 4.76 | 12.46 |
| AEMatter+TTA | 195MiB | 2.06 | 16.89 | 4.24 | 11.72 |
| AEMatter (RWA) | 195MiB | - | - | - | - |
Quantitative results on Transparent-460
| Model Name | Size | MSE | SAD | Grad | Conn |
|---|---|---|---|---|---|
| AEMatter | 195MiB | 6.92 | 122.27 | 27.42 | 112.02 |
Quantitative results on AIM-500
| Model Name | Size | MSE | SAD | Grad | Conn |
|---|---|---|---|---|---|
| AEMatter | 195MiB | 11.69 | 14.76 | 11.20 | 14.20 |
Due to differences in data set preparation, the quantitative results on Distinction-646 and Semantic Image Matting are not shown.
We provide the script eval.py for evaluation.