Deep Learning Slide Captcha

Related tags

Deep LearningDeepLearningSlideCaptcha
Overview

滑动验证码深度学习识别

本项目使用深度学习 YOLOV3 模型来识别滑动验证码缺口，基于 https://github.com/eriklindernoren/PyTorch-YOLOv3 修改。

只需要几百张缺口标注图片即可训练出精度高的识别模型，识别效果样例：

克隆项目

运行命令：

git clone https://github.com/Python3WebSpider/DeepLearningSlideCaptcha.git

数据准备

使用 LabelImg 工具标注自行标注一批数据，大约 200 张以上即可训练出不错的效果。

LabelImg：https://github.com/tzutalin/labelImg

标注要求：

  • 圈出验证码目标滑块区域的完整完整矩形，无需标注源滑块。
  • 目标矩形命名为 target 这个类别。
  • 建议使用 LabelImg 的快捷键提高标注效率。

环境准备

建议在 GPU 环境和虚拟 Python 环境下执行如下命令：

pip3 install -r requirements.txt

预训练模型下载

YOLOV3 的训练要加载预训练模型才能有不错的训练效果，预训练模型下载：

bash prepare.sh

下载完成之后会在 weights 文件夹下出现模型权重文件，供训练使用。

训练

本项目已经提供了标注好的数据集，在 data/captcha，可以直接使用。

如果要训练自己的数据，数据格式准备见：https://github.com/eriklindernoren/PyTorch-YOLOv3#train-on-custom-dataset。

当前数据训练脚本：

bash train.sh

实测 P100 训练时长约 15 秒一个 epoch，大约几分钟即可训练出较好效果。

测试

训练完毕之后会在 checkpoints 文件夹生成 pth 文件，可直接使用模型来预测生成标注结果。

此时 checkpoints 文件夹会生成训练好的 pth 文件。

当前数据测试脚本：

sh detect.sh

该脚本会读取 captcha 下的 test 文件夹所有图片，并将处理后的结果输出到 test 文件夹。

运行结果样例：

Performing object detection:
        + Batch 0, Inference Time: 0:00:00.044223
        + Batch 1, Inference Time: 0:00:00.028566
        + Batch 2, Inference Time: 0:00:00.029764
        + Batch 3, Inference Time: 0:00:00.032430
        + Batch 4, Inference Time: 0:00:00.033373
        + Batch 5, Inference Time: 0:00:00.027861
        + Batch 6, Inference Time: 0:00:00.031444
        + Batch 7, Inference Time: 0:00:00.032110
        + Batch 8, Inference Time: 0:00:00.029131

Saving images:
(0) Image: 'data/captcha/test/captcha_4497.png'
        + Label: target, Conf: 0.99999
(1) Image: 'data/captcha/test/captcha_4498.png'
        + Label: target, Conf: 0.99999
(2) Image: 'data/captcha/test/captcha_4499.png'
        + Label: target, Conf: 0.99997
(3) Image: 'data/captcha/test/captcha_4500.png'
        + Label: target, Conf: 0.99999
(4) Image: 'data/captcha/test/captcha_4501.png'
        + Label: target, Conf: 0.99997
(5) Image: 'data/captcha/test/captcha_4502.png'
        + Label: target, Conf: 0.99999
(6) Image: 'data/captcha/test/captcha_4503.png'
        + Label: target, Conf: 0.99997
(7) Image: 'data/captcha/test/captcha_4504.png'
        + Label: target, Conf: 0.99998
(8) Image: 'data/captcha/test/captcha_4505.png'
        + Label: target, Conf: 0.99998

样例结果：

协议

本项目基于开源 GNU 协议 ，另外本项目不提供任何有关滑动轨迹相关模拟和 JavaScript 逆向分析方案。

本项目仅供学习交流使用，请勿用于非法用途，本人不承担任何法律责任。

如有侵权请联系个人删除，谢谢。

Owner
Python3WebSpider
Python3WebSpider
GitHub Repository
Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"

NeuralSymbolicRegressionThatScales Pytorch implementation and pretrained models for the paper "Neural Symbolic Regression That Scales", presented at I

35 Nov 25, 2022
A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

NeRF Minimal Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Result of Tiny-NeRF RGB Depth

Soumik Rakshit 11 Jul 24, 2022
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

EncT5 (Unofficial) Pytorch Implementation of EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks About Finetune T5 model for classification & r

Jangwon Park 34 Jan 01, 2023
Finding an Unsupervised Image Segmenter in each of your Deep Generative Models

Finding an Unsupervised Image Segmenter in each of your Deep Generative Models Description Recent research has shown that numerous human-interpretable

Luke Melas-Kyriazi 61 Oct 17, 2022
Gesture recognition on Event Data

Event based Gesture Recognition Gesture recognition on Event Data usually involv

2 Feb 14, 2022
Chinese clinical named entity recognition using pre-trained BERT model

Chinese clinical named entity recognition (CNER) using pre-trained BERT model Introduction Code for paper Chinese clinical named entity recognition wi

Xiangyang Li 109 Dec 14, 2022
A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

A selection of State Of The Art research papers (and code) on human trajectory prediction (forecasting). Papers marked with [W] are workshop papers.

Karttikeya Manglam 40 Nov 18, 2022
Codecov coverage standard for Python

Python-Standard Last Updated: 01/07/22 00:09:25 What is this? This is a Python application, with basic unit tests, for which coverage is uploaded to C

Codecov 10 Nov 04, 2022
LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations

LIMEcraft LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations The LIMEcraft algorithm is an explanatory method based on

MI^2 DataLab 4 Aug 01, 2022
Towards Part-Based Understanding of RGB-D Scans

Towards Part-Based Understanding of RGB-D Scans (CVPR 2021) We propose the task of part-based scene understanding of real-world 3D environments: from

26 Nov 23, 2022
Speech Recognition using DeepSpeech2.

deepspeech.pytorch Implementation of DeepSpeech2 for PyTorch using PyTorch Lightning. The repo supports training/testing and inference using the DeepS

Sean Naren 2k Jan 04, 2023
deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and different optimization choices

deep_nn_model_with_only_python_100%_test_accuracy deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and differen

0 Aug 28, 2022
KoCLIP: Korean port of OpenAI CLIP, in Flax

KoCLIP This repository contains code for KoCLIP, a Korean port of OpenAI's CLIP. This project was conducted as part of Hugging Face's Flax/JAX communi

Jake Tae 100 Jan 02, 2023
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
Rethinking Transformer-based Set Prediction for Object Detection

Rethinking Transformer-based Set Prediction for Object Detection Here are the code for the ICCV paper. The code is adapted from Detectron2 and AdelaiD

Zhiqing Sun 62 Dec 03, 2022
Drone detection using YOLOv5

This drone detection system uses YOLOv5 which is a family of object detection architectures and we have trained the model on Drone Dataset. Overview I

Tushar Sarkar 27 Dec 20, 2022
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped

CSWin-Transformer This repo is the official implementation of "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows". Th

Microsoft 409 Jan 06, 2023
Python script that allows you to automatically setup your Growtopia server.

AutoSetup Python script that allows you to automatically setup your Growtopia server. How To Use Firstly, install all the required modules that used i

Aspire 3 Mar 06, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
⚡ H2G-Net for Semantic Segmentation of Histopathological Images

H2G-Net This repository contains the code relevant for the proposed design H2G-Net, which was introduced in the manuscript "Hybrid guiding: A multi-re

André Pedersen 8 Nov 24, 2022