https://github.com/microsoft/yolat-vectorgraphicsrecognition
Source Code of NeurIPS21 and T-PAMI24 paper: Recognizing Vector Graphics without Rasterization
https://github.com/microsoft/yolat-vectorgraphicsrecognition
gnn-model vector-database vector-graphics
Last synced: 9 months ago
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Source Code of NeurIPS21 and T-PAMI24 paper: Recognizing Vector Graphics without Rasterization
- Host: GitHub
- URL: https://github.com/microsoft/yolat-vectorgraphicsrecognition
- Owner: microsoft
- License: mit
- Created: 2021-12-03T07:36:33.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-08-26T15:41:22.000Z (9 months ago)
- Last Synced: 2025-09-02T00:38:01.578Z (9 months ago)
- Topics: gnn-model, vector-database, vector-graphics
- Language: Python
- Homepage:
- Size: 2.39 MB
- Stars: 98
- Watchers: 8
- Forks: 17
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Support: SUPPORT.md
Awesome Lists containing this project
README
# :fire: [NIPS2021, TPAMI2024] YOLaT & YOLaT++: Powerful and Efficient Graph Models for Vector Graphics Recognition
## :scroll: Introduction
[](https://arxiv.org/abs/2111.03281)
This repository is the official PyTorch implementation of our two powerful vector graphics recognition models.
> NeurIPS-2021 [paper](https://arxiv.org/abs/2111.03281): Recognizing Vector Graphics without Rasterization.
> TPAMI-2024 [paper](https://ieeexplore.ieee.org/abstract/document/10508965): Hierarchical Recognizing Vector Graphics and A New Chart-based Vector Graphics Dataset
Rendering vector graphics into pixel arrays can result in significant memory costs or loss of information, as demonstrated in above Figure 1. Additionally, this process discards high-level structural information within the primitives, which is critical for recognition tasks such as identifying corners and contours.
To summarize, we propose You Only Look at Text series (YOLaT & YOLaT++) which addresses issues with raster graphics by taking in textual documents of vector graphics as input.
## Environments
```sh
conda create -n your_env_name python=3.8
conda activate your_env_name
sh deepgcn_env_install.sh
```
## YOLaT
### 1. Data Preparation
#### Floorplans
a) Download and unzip the [Floorplans dataset](http://mathieu.delalandre.free.fr/projects/sesyd/symbols/floorplans.html) to the dataset folder: `data/FloorPlansGraph5_iter`
b) Run the following scripts to prepare the dataset for training/inference.
```sh
cd utils
python svg_utils/build_graph_bbox.py
```
#### Diagrams
a) Download and unzip the [Diagrams dataset](http://mathieu.delalandre.free.fr/projects/sesyd/symbols/diagrams.html) to the dataset folder: `data/diagrams`
b) Run the following scripts to prepare the dataset for training/inference.
```sh
cd utils
python svg_utils/build_graph_bbox_diagram.py
```
### 2. Training & Inference
#### Floorplans
```sh
cd cad_recognition
CUDA_VISIBLE_DEVICES=0 python -u train.py --batch_size 4 --data_dir data/FloorPlansGraph5_iter --phase train --lr 2.5e-4 --lr_adjust_freq 9999999999999999999999999999999999999 --in_channels 5 --n_blocks 2 --n_blocks_out 2 --arch centernet3cc_rpn_gp_iter2 --graph bezier_cc_bb_iter --data_aug true --weight_decay 1e-5 --postname run182_2 --dropout 0.0 --do_mixup 0 --bbox_sampling_step 10
```
#### Diagrams
```sh
cd cad_recognition
CUDA_VISIBLE_DEVICES=0 python -u train.py --batch_size 4 --data_dir data/diagrams --phase train --lr 2.5e-4 --lr_adjust_freq 9999999999999999999999999999999999999 --in_channels 5 --n_blocks 2 --n_blocks_out 2 --arch centernet3cc_rpn_gp_iter2 --graph bezier_cc_bb_iter --data_aug true --weight_decay 1e-5 --postname run182_2 --dropout 0.0 --do_mixup 0 --bbox_sampling_step 5
```
## YOLaT++
YOLaT++ is introduced, characterized by a hierarchical structure designed for VGs, spanning three levels: **Primitive, Curve, and Point**. Additionally, YOLaT++ employs a position-aware enhancement strategy to effectively differentiate similar primitives.
## Citation
BibTex:
```
@inproceedings{jiang2021recognizing,
title={{Recognizing Vector Graphics without Rasterization}},
author={Jiang, Xinyang and Liu, Lu and Shan, Caihua and Shen, Yifei and Dong, Xuanyi and Li, Dongsheng},
booktitle={Proceedings of Advances in Neural Information Processing Systems (NIPS)},
volume={34},
number={},
pages={},
year={2021}}
@article{journals/pami/DouJLYSSDWLZ24,
author = {Shuguang Dou and Xinyang Jiang and Lu Liu and Lu Ying and Caihua Shan and Yifei Shen and Xuanyi Dong and Yun Wang and Dongsheng Li and Cairong Zhao},
title = {Hierarchically Recognizing Vector Graphics and {A} New Chart-Based
Vector Graphics Dataset},
journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
volume = {46},
number = {12},
pages = {7556--7573},
year = {2024},
doi = {10.1109/TPAMI.2024.3394298},
}
```
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# Related Dataset
[Benchmark for VG-based Detection and Chart Understanding](https://github.com/Vill-Lab/2024-TPAMI-VGDCU)