[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

Related tags

Deep LearningCORE-Text
Overview

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

This repository is the official PyTorch implementation of CORE-Text, and contains demo training and evaluation scripts.

CORE-Text

Requirements

Training Demo

Base (Mask R-CNN)

To train Base (Mask R-CNN) on a single node with 4 gpus, run:

#!/usr/bin/env bash

GPUS=4
PORT=${PORT:-29500}
PYTHON=${PYTHON:-"python"}

CONFIG=configs/icdar2017mlt/base.py
WORK_DIR=work_dirs/mask_rcnn_r50_fpn_train_base

$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS \
                                    --nnodes=1 --node_rank=0 --master_addr="localhost" \
                                    --master_port=$PORT \
                                    tools/train.py \
                                    $CONFIG \
                                    --no-validate \
                                    --launcher pytorch \
                                    --work-dir ${WORK_DIR} \
                                    --seed 0

VRM

To train VRM on a single node with 4 gpus, run:

#!/usr/bin/env bash

GPUS=4
PORT=${PORT:-29500}
PYTHON=${PYTHON:-"python"}

CONFIG=configs/icdar2017mlt/vrm.py
WORK_DIR=work_dirs/mask_rcnn_r50_fpn_train_vrm

$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS \
                                    --nnodes=1 --node_rank=0 --master_addr="localhost" \
                                    --master_port=$PORT \
                                    tools/train.py \
                                    $CONFIG \
                                    --no-validate \
                                    --launcher pytorch \
                                    --work-dir ${WORK_DIR} \
                                    --seed 0

CORE

To train CORE (ours) on a single node with 4 gpus, run:

#!/usr/bin/env bash

GPUS=4
PORT=${PORT:-29500}
PYTHON=${PYTHON:-"python"}

# pre-training
CONFIG=configs/icdar2017mlt/core_pretrain.py
WORK_DIR=work_dirs/mask_rcnn_r50_fpn_train_core_pretrain

$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS \
                                    --nnodes=1 --node_rank=0 --master_addr="localhost" \
                                    --master_port=$PORT \
                                    tools/train.py \
                                    $CONFIG \
                                    --no-validate \
                                    --launcher pytorch \
                                    --work-dir ${WORK_DIR} \
                                    --seed 0

# training
CONFIG=configs/icdar2017mlt/core.py
WORK_DIR=work_dirs/mask_rcnn_r50_fpn_train_core

$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS \
                                    --nnodes=1 --node_rank=0 --master_addr="localhost" \
                                    --master_port=$PORT \
                                    tools/train.py \
                                    $CONFIG \
                                    --no-validate \
                                    --launcher pytorch \
                                    --work-dir ${WORK_DIR} \
                                    --seed 0

Evaluation Demo

GPUS=4
PORT=${PORT:-29500}
CONFIG=path/to/config
CHECKPOINT=path/to/checkpoint

python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
    ./tools/test.py $CONFIG $CHECKPOINT --launcher pytorch \
    --eval segm \
    --not-encode-mask \
    --eval-options "jsonfile_prefix=path/to/work_dir/results/eval" "gt_path=data/icdar2017mlt/icdar2017mlt_gt.zip"

Dataset Format

The structure of the dataset directory is shown as following, and we provide the COCO-format label (ICDAR2017_train.json and ICDAR2017_val.json) and the ground truth zipfile (icdar2017mlt_gt.zip) for training and evaluation.

data
└── icdar2017mlt
    ├── annotations
    |   ├── ICDAR2017_train.json
    |   └── ICDAR2017_val.json
    ├── icdar2017mlt_gt.zip
    └── image
         ├── train
         └── val

Results

Our model achieves the following performance on ICDAR 2017 MLT val set. Note that the results are slightly different (~0.1%) from what we reported in the paper, because we reimplement the code based on the open-source mmdetection.

Method Backbone Training set Test set Hmean Precision Recall Download
Base (Mask R-CNN) ResNet50 ICDAR 2017 MLT Train ICDAR 2017 MLT Val 0.800 0.828 0.773 model | log
VRM ResNet50 ICDAR 2017 MLT Train ICDAR 2017 MLT Val 0.812 0.853 0.774 model | log
CORE (ours) ResNet50 ICDAR 2017 MLT Train ICDAR 2017 MLT Val 0.821 0.872 0.777 model | log

Citation

@inproceedings{9428457,
  author={Lin, Jingyang and Pan, Yingwei and Lai, Rongfeng and Yang, Xuehang and Chao, Hongyang and Yao, Ting},
  booktitle={2021 IEEE International Conference on Multimedia and Expo (ICME)},
  title={Core-Text: Improving Scene Text Detection with Contrastive Relational Reasoning},
  year={2021},
  pages={1-6},
  doi={10.1109/ICME51207.2021.9428457}
}
Owner
Jingyang Lin
Graduate student @ SYSU.
Jingyang Lin
GitHub Repository
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Pytorch Squeeznet Pytorch implementation of Squeezenet model as described in https://arxiv.org/abs/1602.07360 on cifar-10 Data. The definition of Sque

gaurav pathak 86 Oct 28, 2022
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization

Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization This repository contains the code for the BBI optimizer, introduced in the p

G. Bruno De Luca 5 Sep 06, 2022
Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

DialogBERT This is a PyTorch implementation of the DialogBERT model described in DialogBERT: Neural Response Generation via Hierarchical BERT with Dis

Xiaodong Gu 67 Jan 06, 2023
Large-scale language modeling tutorials with PyTorch

Large-scale language modeling tutorials with PyTorch 안녕하세요. 저는 TUNiB에서 머신러닝 엔지니어로 근무 중인 고현웅입니다. 이 자료는 대규모 언어모델 개발에 필요한 여러가지 기술들을 소개드리기 위해 마련하였으며 기본적으로

TUNiB 172 Dec 29, 2022
A PaddlePaddle version image model zoo.

Paddle-Image-Models English | 简体中文 A PaddlePaddle version image model zoo. Install Package Install by pip： $ pip install ppim Install by wheel package

AgentMaker 131 Dec 07, 2022
tensorflow code for inverse face rendering

InverseFaceRender This is tensorflow code for our project: Learning Inverse Rendering of Faces from Real-world Videos. (https://arxiv.org/abs/2003.120

Yuda Qiu 18 Nov 16, 2022
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
A facial recognition doorbell system using a Raspberry Pi

Facial Recognition Doorbell This project expands on the person-detecting doorbell system to allow it to identify faces, and announce names accordingly

rydercalmdown 22 Apr 15, 2022
Megaverse is a new 3D simulation platform for reinforcement learning and embodied AI research

Megaverse Megaverse is a new 3D simulation platform for reinforcement learning and embodied AI research. The efficient design of the engine enables ph

Aleksei Petrenko 191 Dec 23, 2022
Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment

Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment.

MT Schmitz 2 Feb 11, 2022
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an

0 Nov 10, 2021
TAug :: Time Series Data Augmentation using Deep Generative Models

TAug :: Time Series Data Augmentation using Deep Generative Models Note!!! The package is under development so be careful for using in production! Fea

35 Dec 06, 2022
End-to-End Object Detection with Fully Convolutional Network

This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.

472 Dec 22, 2022
An implementation of the proximal policy optimization algorithm

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment

logit-adj-pytorch PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment This code implements the paper: Long-tail Learning via

Chamuditha Jayanga 53 Dec 23, 2022
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN)

Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN) This is the implementation of the paper Multi-Age

Future Power Networks 83 Jan 06, 2023
Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore

[AI6122] Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instructor of this course

HT. Li 5 Sep 12, 2022
Merlion: A Machine Learning Framework for Time Series Intelligence

Merlion: A Machine Learning Library for Time Series Table of Contents Introduction Installation Documentation Getting Started Anomaly Detection Foreca

Salesforce 2.8k Dec 30, 2022
Activity image-based video retrieval

Cross-modal-retrieval Our approach is focus on Activity Image-to-Video Retrieval (AIVR) task. The compared methods are state-of-the-art single modalit

BCMI 75 Oct 21, 2021
EfficientMPC - Efficient Model Predictive Control Implementation

efficientMPC Efficient Model Predictive Control Implementation The original algo

Vin 8 Dec 04, 2022