| --- |
| license: mit |
| library_name: pytorch |
| pipeline_tag: image-classification |
| tags: |
| - texture |
| - tileability |
| - metric |
| - cvpr2024 |
| --- |
| |
| # TexTile: A Differentiable Metric for Texture Tileability |
|
|
| Pretrained weights for **TexTile** [CVPR 2024], a differentiable metric that measures how well a texture image can be tiled with itself without visible seams or repeating artifacts. |
|
|
| [Carlos Rodriguez-Pardo](https://carlosrodriguezpardo.es/), [Dan Casas](https://dancasas.github.io/), [Elena Garces](https://www.elenagarces.es/), [Jorge Lopez-Moreno](http://www.jorg3.com/) |
|
|
| - Paper: [arXiv:2403.12961](https://arxiv.org/abs/2403.12961) |
| - Code: [github.com/crp94/textile](https://github.com/crp94/textile) |
|
|
| ## Files |
|
|
| | File | Description | |
| | --- | --- | |
| | `model.safetensors` | Weights in safetensors format. Loaded by the `textile-metric` package. | |
| | `textile_v3.pth` | Original PyTorch state dict (identical weights). | |
|
|
| ## Usage |
|
|
| ```bash |
| pip install textile-metric |
| ``` |
|
|
| ```python |
| import textile |
| from textile.utils.image_utils import read_and_process_image |
| |
| loss_textile = textile.Textile() # downloads model.safetensors from this repo |
| image = read_and_process_image(YOUR_PATH) |
| textile_value = loss_textile(image) |
| ``` |
|
|
| The architecture is a ConvNeXt-Base with added linear self-attention layers; see `textile/utils/create_model.py` in the code repository. |
|
|
| ## Citation |
|
|
| ``` |
| @inproceedings{Rodriguez-Pardo_2024_CVPR, |
| author = {Rodriguez-Pardo, Carlos and Casas, Dan and Garces, Elena and Lopez-Moreno, Jorge}, |
| title = {TexTile: A Differentiable Metric for Texture Tileability}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year = {2024} |
| } |
| ``` |
|
|
