metadata
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, Dan Casas, Elena Garces, Jorge Lopez-Moreno
- Paper: arXiv:2403.12961
- Code: 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
pip install textile-metric
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}
}