Pytorch re-implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022)

Related tags

Deep LearningSwinTextSpotter
Overview

SwinTextSpotter

This is the pytorch implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022). The paper is available at this link.

Models

SWINTS-swin-english-pretrain [config] | model_Google Drive | model_BaiduYun PW: 954t

SWINTS-swin-Total-Text [config] | model_Google Drive | model_BaiduYun PW: tf0i

SWINTS-swin-ctw [config] | model_Google Drive | model_BaiduYun PW: 4etq

SWINTS-swin-icdar2015 [config] | model_Google Drive | model_BaiduYun PW: 3n82

SWINTS-swin-ReCTS [config] | model_Google Drive | model_BaiduYun PW: a4be

SWINTS-swin-vintext [config] | model_Google Drive | model_BaiduYun PW: slmp

Installation

  • Python=3.8
  • PyTorch=1.8.0, torchvision=0.9.0, cudatoolkit=11.1
  • OpenCV for visualization

Steps

  1. Install the repository (we recommend to use Anaconda for installation.)
conda create -n SWINTS python=3.8 -y
conda activate SWINTS
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install opencv-python
pip install scipy
pip install shapely
pip install rapidfuzz
pip install timm
pip install Polygon3
git clone https://github.com/mxin262/SwinTextSpotter.git
cd SwinTextSpotter
python setup.py build develop
  1. dataset path
datasets
|_ totaltext
|  |_ train_images
|  |_ test_images
|  |_ totaltext_train.json
|  |_ weak_voc_new.txt
|  |_ weak_voc_pair_list.txt
|_ mlt2017
|  |_ train_images
|  |_ annotations/icdar_2017_mlt.json
.......

Downloaded images

Downloaded label[Google Drive] [BaiduYun] PW: 46vd

Downloader lexicion[Google Drive] and place it to corresponding dataset.

You can also prepare your custom dataset following the example scripts. [example scripts]

Totaltext

To evaluate on Total Text, CTW1500, ICDAR2015, first download the zipped annotations with

cd datasets
mkdir evaluation
cd evaluation
wget -O gt_ctw1500.zip https://cloudstor.aarnet.edu.au/plus/s/xU3yeM3GnidiSTr/download
wget -O gt_totaltext.zip https://cloudstor.aarnet.edu.au/plus/s/SFHvin8BLUM4cNd/download
wget -O gt_icdar2015.zip https://drive.google.com/file/d/1wrq_-qIyb_8dhYVlDzLZTTajQzbic82Z/view?usp=sharing
wget -O gt_vintext.zip https://drive.google.com/file/d/11lNH0uKfWJ7Wc74PGshWCOgSxgEnUPEV/view?usp=sharing
  1. Pretrain SWINTS (e.g., with Swin-Transformer backbone)
python projects/SWINTS/train_net.py \
  --num-gpus 8 \
  --config-file projects/SWINTS/configs/SWINTS-swin-pretrain.yaml
  1. Fine-tune model on the mixed real dataset
python projects/SWINTS/train_net.py \
  --num-gpus 8 \
  --config-file projects/SWINTS/configs/SWINTS-swin-mixtrain.yaml
  1. Fine-tune model
python projects/SWINTS/train_net.py \
  --num-gpus 8 \
  --config-file projects/SWINTS/configs/SWINTS-swin-finetune-totaltext.yaml
  1. Evaluate SWINTS (e.g., with Swin-Transformer backbone)
python projects/SWINTS/train_net.py \
  --config-file projects/SWINTS/configs/SWINTS-swin-finetune-totaltext.yaml \
  --eval-only MODEL.WEIGHTS ./output/model_final.pth
  1. Visualize the detection and recognition results (e.g., with ResNet50 backbone)
python demo/demo.py \
  --config-file projects/SWINTS/configs/SWINTS-swin-finetune-totaltext.yaml \
  --input input1.jpg \
  --output ./output \
  --confidence-threshold 0.4 \
  --opts MODEL.WEIGHTS ./output/model_final.pth

Example results:

Acknowlegement

Adelaidet, Detectron2, ISTR, SwinT_detectron2, Focal-Transformer and MaskTextSpotterV3.

Citation

If our paper helps your research, please cite it in your publications:

@article{huang2022swints,
  title = {SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition},
  author = {Mingxin Huang and YuLiang liu and Zhenghao Peng and Chongyu Liu and Dahua Lin and Shenggao Zhu and Nicholas Yuan and Kai Ding and Lianwen Jin},
  journal={arXiv preprint arXiv:2203.10209},
  year = {2022}
}

Copyright

For commercial purpose usage, please contact Dr. Lianwen Jin: [email protected]

Copyright 2019, Deep Learning and Vision Computing Lab, South China China University of Technology. http://www.dlvc-lab.net

Owner
mxin262
mxin262
GitHub Repository
A nutritional label for food for thought.

Lexiscore As a first effort in tackling the theme of information overload in content consumption, I've been working on the lexiscore: a nutritional la

Paul Bricman 34 Nov 08, 2022
RoMA: Robust Model Adaptation for Offline Model-based Optimization

RoMA: Robust Model Adaptation for Offline Model-based Optimization Implementation of RoMA: Robust Model Adaptation for Offline Model-based Optimizatio

9 Oct 31, 2022
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

DeepCTR DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can

浅梦 6.6k Jan 08, 2023
Official implementation of ACMMM'20 paper 'Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework'

Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework Official code for paper, Self-supervised Video Representation Le

Li Tao 103 Dec 21, 2022
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

Rishikesh S 15 Aug 20, 2022
This repository provides data for the VAW dataset as described in the CVPR 2021 paper titled "Learning to Predict Visual Attributes in the Wild"

Visual Attributes in the Wild (VAW) This repository provides data for the VAW dataset as described in the CVPR 2021 Paper: Learning to Predict Visual

Adobe Research 36 Dec 30, 2022
Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

simplified_mediapipe_face_landmarks Extracts essential Mediapipe face landmarks and arranges them in a sequenced order. The default 478 Mediapipe face

Irfan 13 Oct 04, 2022
UnpNet - Rethinking 3-D LiDAR Point Cloud Segmentation(IEEE TNNLS)

UnpNet Citation Please cite the following paper if you use this repository in your reseach. @article {PMID:34914599, Title = {Rethinking 3-D LiDAR Po

Shijie Li 4 Jul 15, 2022
Image Captioning using CNN and Transformers

Image-Captioning Keras/Tensorflow Image Captioning application using CNN and Transformer as encoder/decoder. In particulary, the architecture consists

24 Dec 28, 2022
TensorFlow implementation of "Variational Inference with Normalizing Flows"

[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co

YeongHyeon Park 7 Jun 08, 2022
A Comprehensive Study on Learning-Based PE Malware Family Classification Methods

A Comprehensive Study on Learning-Based PE Malware Family Classification Methods Datasets Because of copyright issues, both the MalwareBazaar dataset

8 Oct 21, 2022
Feature extraction made simple with torchextractor

torchextractor: PyTorch Intermediate Feature Extraction Introduction Too many times some model definitions get remorselessly copy-pasted just because

Antoine Broyelle 89 Oct 31, 2022
iBOT: Image BERT Pre-Training with Online Tokenizer

Image BERT Pre-Training with iBOT Official PyTorch implementation and pretrained models for paper iBOT: Image BERT Pre-Training with Online Tokenizer.

Bytedance Inc. 435 Jan 06, 2023
AttGAN: Facial Attribute Editing by Only Changing What You Want (IEEE TIP 2019)

News 11 Jan 2020: We clean up the code to make it more readable! The old version is here: v1. AttGAN TIP Nov. 2019, arXiv Nov. 2017 TensorFlow impleme

Zhenliang He 568 Dec 14, 2022
A python code to convert Keras pre-trained weights to Pytorch version

Weights_Keras_2_Pytorch 最近想在Pytorch项目里使用一下谷歌的NIMA，但是发现没有预训练好的pytorch权重，于是整理了一下将Keras预训练权重转为Pytorch的代码，目前是支持Keras的Conv2D, Dense, DepthwiseConv2D, Batch

Liu Hengyu 2 Dec 16, 2021
IMBENS: class-imbalanced ensemble learning in Python.

IMBENS: class-imbalanced ensemble learning in Python. Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [知乎/Zhihu] [中文README] [a

Zhining Liu 176 Jan 04, 2023
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

README clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation CVPR 2021 Authors: Suprosanna Shit and Johannes C. Paetzo

110 Dec 29, 2022
PyTorch implementation of popular datasets and models in remote sensing

PyTorch Remote Sensing (torchrs) (WIP) PyTorch implementation of popular datasets and models in remote sensing tasks (Change Detection, Image Super Re

isaac 222 Dec 28, 2022
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation This repository contains the source code of our paper, ESPNet (acc

Sachin Mehta 515 Dec 13, 2022