某学校选课系统GIF验证码数据集 + Baseline模型 + 上下游相关工具

Related tags

Deep Learningelective-dataset-2021spring
Overview

elective-dataset-2021spring

某学校2021春季选课系统GIF验证码数据集（29338张） + 准确率98.4%的Baseline模型 + 上下游相关工具。

数据集采用 知识共享署名-非商业性使用 4.0 国际许可协议 进行许可。

Baseline模型和上下游相关工具采用 MIT License 进行许可。

数据集

dataset/ 目录包含了收集到的所有带标签验证码数据，共29338张。

  • dataset/manual： 人工标注的带标签验证码GIF数据集，标签经过了elective验证因此都是正确的。共5471张。
  • dataset/auto-corr、dataset/auto-fail-tagged： 模型自动标注的带标签验证码GIF数据集，其中 auto-corr 是识别正确（通过了elective验证）的部分，auto-fail-tagged 是识别错误然后手工重新标注的部分（此部分不保证正确性）。共22931（正确）+936（错误）张。

使用时请注意，由于 GitHub 的限制：

  • auto-fail-tagged 在仓库中存储为7-zip压缩包；
  • manual 在仓库中存储为7个不超过48MB的7-zip分卷；
  • auto-corr 没有存储在仓库中，而是压缩为14个不超过95MB的7-zip分卷放在了 Release页面。

Baseline 模型

baseline/ 目录包含一个简易的验证码识别模型。

此模型进行了提取关键帧、基于OpenCV的图像增强以及基于CNN的分类器等一系列工作以完成识别。

将训练集和测试集图片分别放入 set-train 和 set-test 后运行 train.py 进行训练，用一块TITAN RTX训练需要几分钟的时间。

用大约一万张图片训练好的 checkpoints/model_29.pth 能达到 98.4% 的整体精确度。

predict_bootstrap.py 在elective系统上测试当前模型，将检验正确的带标签图片放入 bootstrap_img_succ 目录，错误的图片放入 bootstrap_img_fail 目录。

上下游相关工具

  • crawl/： 验证码众包标注平台，可以从elective爬取验证码、辅助多名用户同时标注、检验正确性后将正确的数据放入 img_correct 目录。检验错误的验证码将被抛弃，这是初期的一个设计失误，这样将使得数据集的分布与真实分布有偏差。
  • retag/： 手工标注模型识别错误数据的工具。从 bootstrap_img_fail 读取标注错误图片，人工输入正确标注后移动到 bootstrap_img_fail_tagged。
  • serve/： 提供在线验证码识别服务的 HTTP RPC 服务器。POST /fire 并传入base64编码的验证码GIF来进行识别。

数据处理过程

首先，我们设立了众包标注平台，多名志愿者累计标注了超过五千张验证码。

有了这些数据后，我们利用OpenCV进行了简单的图片增强、二值化、分字、裁切，然后随手糊了一个简单的CNN网络来识别。在随意调参之后，模型的整体（四个字）准确率接近95%。

然后，我们利用此模型来对数据集进行自举：爬取验证码后调用模型识别然后检验正确性，其中识别错误的部分手工标注。这样我们可以轻易地扩大数据集的规模，从而提升模型效果。

经过了更多的随意调参，模型的整体准确率可以达到98.4%。因为继续提升准确率意义不大，就没有继续优化。考虑到 PyTorch 安装比较麻烦，模型不易于部署到用户的设备上，我们实现了一个 HTTP API 可以用于云端识别。

相关工作

by Elector Quartet (按字典序的倒序 @xmcp, @SpiritedAwayCN, @Rabbit, @gzz)

You might also like...
Owner
xmcp
叶氏筛法第 NaN 代传人
xmcp
GitHub Repository
This is the official repository of XVFI (eXtreme Video Frame Interpolation)

XVFI This is the official repository of XVFI (eXtreme Video Frame Interpolation), https://arxiv.org/abs/2103.16206 Last Update: 20210607 We provide th

Jihyong Oh 195 Dec 29, 2022
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model Baris Gecer 1, Binod Bhattarai 1

Baris Gecer 190 Dec 29, 2022
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

MASA-SR Official PyTorch implementation of our CVPR2021 paper MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Re

DV Lab 126 Dec 20, 2022
Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.

Uncertainty-based OOD detection Exploring the link between uncertainty estimates obtained by "exact" Bayesian inference and out-of-distribution (OOD)

Christian Henning 1 Nov 05, 2022
SphereFace: Deep Hypersphere Embedding for Face Recognition

SphereFace: Deep Hypersphere Embedding for Face Recognition By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song License SphereFa

Weiyang Liu 1.5k Dec 29, 2022
Hippocampal segmentation using the UNet network for each axis

Hipposeg Hippocampal segmentation using the UNet network for each axis, inspired by https://github.com/MICLab-Unicamp/e2dhipseg Red: False Positive Gr

Juan Carlos Aguirre Arango 0 Sep 02, 2021
The InterScript dataset contains interactive user feedback on scripts generated by a T5-XXL model.

Interscript The Interscript dataset contains interactive user feedback on a T5-11B model generated scripts. Dataset data.json contains the data in an

AI2 8 Dec 01, 2022
Title: Graduate-Admissions-Predictor

The purpose of this project is create a predictive model capable of identifying the probability of a person securing an admit based on their personal profile parameters. Simplified visualisations hav

Akarsh Singh 1 Jan 26, 2022
Code for the published paper : Learning to recognize rare traffic sign

Improving traffic sign recognition by active search This repo contains code for the paper : "Learning to recognise rare traffic signs" How to use this

samsja 4 Jan 05, 2023
Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.

The goal is to classify different birds species based on their songs/calls. Spectrograms have been extracted from the audio samples and used as features for classification.

Aditya Dutt 9 Dec 27, 2022
Federated Learning - Including common test models for federated learning, like CNN, Resnet18 and lstm, controlled by different parser

Federated_Learning 💻 This projest include common test models for federated lear

TianyuQi 10 Dec 11, 2022
Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation

DynaBOA Code repositoty for the paper: Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation Shanyan Guan, Jingwei Xu, Michell

197 Jan 07, 2023
Implementation of U-Net and SegNet for building segmentation

Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te

Martin.w-e 3 Dec 07, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 08, 2022
pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 04, 2022
PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021.

IBRNet: Learning Multi-View Image-Based Rendering PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021. IBRN

Google Interns 371 Jan 03, 2023
Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization".

SAPE Project page Paper Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization". Environment Cre

36 Dec 09, 2022
Categorical Depth Distribution Network for Monocular 3D Object Detection

CaDDN CaDDN is a monocular-based 3D object detection method. This repository is based off of [OpenPCDet]. Categorical Depth Distribution Network for M

Toronto Robotics and AI Laboratory 289 Jan 05, 2023
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

CPT This repository contains code and checkpoints for CPT. CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Gener

fastNLP 341 Dec 29, 2022
Full body anonymization - Realistic Full-Body Anonymization with Surface-Guided GANs

Realistic Full-Body Anonymization with Surface-Guided GANs This is the official

Håkon Hukkelås 30 Nov 18, 2022