Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

Last update: Dec 20, 2022

Overview

YOLaT-VectorGraphicsRecognition

This repository is the official PyTorch implementation of our NeurIPS-2021 paper: Recognizing Vector Graphics without Rasterization.

Environments

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 to the dataset folder: data/FloorPlansGraph5_iter

b) Run the following scripts to prepare the dataset for training/inference.

python utils/svg_utils/build_graph_bbox.py

Diagrams

a) Download and unzip the Diagrams dataset to the dataset folder: data/diagrams

b) Run the following scripts to prepare the dataset for training/inference.

python utils/svg_utils/build_graph_bbox_diagram.py

2. Training & Inference

Floorplans

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

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

Citation

  @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}}

Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

Related tags

Overview

YOLaT-VectorGraphicsRecognition

Environments

YOLaT

1. Data Preparation

Floorplans

Diagrams

2. Training & Inference

Floorplans

Diagrams

Citation

Owner

Microsoft

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Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

Related tags

Overview

YOLaT-VectorGraphicsRecognition

Environments

YOLaT

1. Data Preparation

Floorplans

Diagrams

2. Training & Inference

Floorplans

Diagrams

Citation

Owner

Microsoft

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Real-time multi-object tracker using YOLO v5 and deep sort

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

Lipschitz-constrained Unsupervised Skill Discovery

Notebook and code to synthesize complex and highly dimensional datasets using Gretel APIs.

DeLag: Detecting Latency Degradation Patterns in Service-based Systems

Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

Code for Paper: Self-supervised Learning of Motion Capture

GPU-Accelerated Deep Learning Library in Python

Franka Emika Panda manipulator kinematics&dynamics simulation

Graph parsing approach to structured sentiment analysis.

[NeurIPS'20] Multiscale Deep Equilibrium Models

RealTime Emotion Recognizer for Machine Learning Study Jam's demo

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

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Credit fraud detection in Python using a Jupyter Notebook

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