PI-DECODER

Introduction

PI-DECODER, a decoder structure designed for Precise Iris Segmentation and Location. The decoder structure is shown below:

Please check technical paper.pdf in the "reference" subfolder for more details.

How to use?

For african dataset, you can enter the following script on your terminal:

python main.py --mode test --model_path ./models/african_best.pth --test_mode 1 --train_dataset african

Then you have iris mask, pupil mask and outer iris mask that are predicted by the input images. At the same time, the relevant index data will be displayed on your terminal.

(ijcb) PS F:\workspace\code\pytorch\PI-DECODER> python main.py --mode test --model_path ./models/african_best.pth --
test_mode 1 --train_dataset african
Namespace(batch_size=1, beta1=0.9, beta2=0.999, img_size=(640, 640), lr=0.0002, mode='test', model_path='./models/af
rican_best.pth', num_epochs=100, num_workers=2, result_path='./result/', test_mode=1, test_path='./dataset/test/', t
rain_dataset='african', train_path='./dataset/train/', valid_path='./dataset/valid/')
image count in train path :5
image count in valid path :5
image count in test path :40
Using Model: PI-DECODER
0.0688 seconds per image

----------------------------------------------------------------------------------------------------------------
|evaluation     |e1(%)          |e2(%)          |miou(%)        |f1(%)          |miou_back      |f1_back        |
----------------------------------------------------------------------------------------------------------------
|iris seg       |0.384026       |0.192013       |91.175200      |95.350625      |95.386805      |97.574698      |
|iris mask      |0.569627       |0.284813       |93.159855      |96.430411      |96.270919      |98.060105      |
|pupil mask     |0.078793       |0.039396       |93.138878      |96.409347      |96.529547      |98.184718      |
----------------------------------------------------------------------------------------------------------------
|average        |0.344149       |0.172074       |92.491311      |96.063461      |96.062424      |97.939840      |
----------------------------------------------------------------------------------------------------------------

Besides, if you don't have groud-truth files or just want to save the results, use test mode 2.

python main.py --mode test --model_path ./models/african_best.pth --test_mode 2 --train_dataset african

Requirements

The whole experiment was run on the NVIDIA RTX 3060. The following are recommended environment configurations.

matplotlib        3.3.4
numpy             1.19.5
opencv-python     4.5.1.48
pandas            1.1.5
Pillow            8.1.2
pip               21.0.1
pyparsing         2.4.7
python-dateutil   2.8.1
pytz              2021.1
scipy             1.5.4
setuptools        52.0.0.post20210125
six               1.15.0
thop              0.0.31.post2005241907
torch             1.7.0+cu110
torchstat         0.0.7
torchsummary      1.5.1
torchvision       0.8.1+cu110

precise iris segmentation

Related tags

Overview

PI-DECODER

Introduction

How to use?

Requirements

Owner

Chinese real time voice cloning (VC) and Chinese text to speech (TTS).

Twitter-NLP-Analysis - Twitter Natural Language Processing Analysis

Uses Google's gTTS module to easily create robo text readin' on command.

Healthsea is a spaCy pipeline for analyzing user reviews of supplementary products for their effects on health.

An open source library for deep learning end-to-end dialog systems and chatbots.

Experiments in converting wikidata to ftm

An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations

Kerberoast with ACL abuse capabilities

A script that automatically creates a branch name using google translation api and jira api

Language-Agnostic SEntence Representations

This is a project of data parallel that running on NLP tasks.

Translation to python of Chris Sims' optimization function

Tool to add main subject to items on Wikidata using a WMFs CirrusSearch for named entity recognition or a manually supplied list of QIDs

A Streamlit web app that generates Rick and Morty stories using GPT2.

This project converts your human voice input to its text transcript and to an automated voice too.

Train and use generative text models in a few lines of code.

A notebook that shows how to import the IITB English-Hindi Parallel Corpus from the HuggingFace datasets repository

Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper

Script to download some free japanese lessons in portuguse from NHK

A Python/Pytorch app for easily synthesising human voices