| ## Model Details | |
| This model is a port of the ViTMatte models, which are trained and tested on the Composition-1k and Distinctions-646 datasets. This port focuses on the performance and accuracy of the models. | |
| Note: The porting of the model is for the convenience of use, and to better promote and learn from this excellent open-source project. | |
| ## Usage | |
| This model aims to perform various image processing tasks, such as image segmentation, object recognition, and object detection. | |
| ## Training Data | |
| The model undergoes training and validation using two datasets: | |
| - Composition-1k, this dataset used for training and testing, includes 1000 samples. | |
| - Distinctions-646, this dataset includes 646 samples and is used for model validation. | |
| ## Training Procedure | |
| The model is trained using the gradient descent algorithm and evaluates its performance using the following four metrics: | |
| - SAD (Sum of Absolute Differences) | |
| - MSE (Mean Squared Error) | |
| - Grad (Gradient) | |
| - Conn (Connectivity) | |
| ## Performance | |
| The models have shown the following performance on the two datasets: | |
| On the Composition-1k dataset: | |
| | Model | SAD | MSE | Grad | Conn | | |
| |------|----|----|-----|-----| | |
| | ViTMatte-S | 21.46 | 3.3 | 7.24 | 16.21 | | |
| | ViTMatte-B | 20.33 | 3.0 | 6.74 | 14.78 | | |
| On the Distinctions-646 dataset: | |
| | Model | SAD | MSE | Grad | Conn | | |
| |------|----|----|-----|-----| | |
| | ViTMatte-S | 21.22 | 2.1 | 8.78 | 17.55 | | |
| | ViTMatte-B | 17.05 | 1.5 | 7.03 | 12.95 | | |
| Both models perform well on these datasets, with ViTMatte-B outperforming ViTMatte-S on most evaluation metrics. | |
| ## Disclaimer | |
| This model is ported from [lufficc's ViTMatte](https://github.com/hustvl/ViTMatte) project. All original rights belong to [lufficc](https://github.com/lufficc). | |
| ## Citation | |
| If you use these models, please cite the original author and project: https://github.com/hustvl/ViTMatte | |
| Thank you for using these models. If you encounter any issues or have any feedback during your usage, please raise them on the original GitHub project page of the author. |