TextBPN

Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection； Accepted by ICCV2021.

Note: The complete code (including training and testing) will be released in TextBPN V2. Relevant work is advancing, and those who are interested in our work can pay more attention to the updates here.

1.Prerequisites t

python 3.9;
PyTorch 1.7.0;
Numpy >=1.2.0
CUDA 11.1;
GCC >=10.0;
NVIDIA GPU(with 11G or larger GPU memory for inference);

2.Dataset Links

3.Models

Total-Text model (pretrained on ICDAR2017-MLT)
CTW-1500 model (pretrained on ICDAR2017-MLT)
MSRA-TD500 model (pretrained on ICDAR2017-MLT)

4.Running Evaluation

run:

sh eval.sh

The details are as follows:

#!/bin/bash
##################### Total-Text ###################################
# test_size=[640,1024]--cfglib/option
CUDA_LAUNCH_BLOCKING=1 python eval_textBPN.py --exp_name Totaltext --checkepoch 390 --dis_threshold 0.3 --cls_threshold 0.825 --test_size 640 1024 --gpu 1

###################### CTW-1500 ####################################
# test_size=[640,1024]--cfglib/option
# CUDA_LAUNCH_BLOCKING=1 python eval_textBPN.py --exp_name Ctw1500 --checkepoch 560 --dis_threshold 0.3 --cls_threshold 0.8 --test_size 640 1024 --gpu 1

#################### MSRA-TD500 ######################################
# test_size=[640,1024]--cfglib/option
#CUDA_LAUNCH_BLOCKING=1 python eval_textBPN.py --exp_name TD500 --checkepoch 680 --dis_threshold 0.3 --cls_threshold 0.925 --test_size 640 1024 --gpu 1

TextBPN Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection

Related tags

Overview

TextBPN

1.Prerequisites t

2.Dataset Links

3.Models

4.Running Evaluation

5.Experiments results

Owner

S.X.Zhang

GUPNet - Geometry Uncertainty Projection Network for Monocular 3D Object Detection

Implementing DropPath/StochasticDepth in PyTorch

Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

DeepGNN is a framework for training machine learning models on large scale graph data.

Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations"

A copy of Ares that costs 30 fucking dollars.

Code and data (Incidents Dataset) for ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild".

[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

A toy project using OpenCV and PyMunk

The official implementation of Theme Transformer

Code-free deep segmentation for computational pathology

The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Generate vibrant and detailed images using only text.

Good Semi-Supervised Learning That Requires a Bad GAN

PyTorch implementation of saliency map-aided GAN for Auto-demosaic+denosing

PyTorch implementation of Pointnet2/Pointnet++

This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures using receptive field analysis (RFA) and create graph visualizations of your architecture.

Face and Body Tracking for VRM 3D models on the web.

Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections