precise iris segmentation

Overview

PI-DECODER

Introduction

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

PI-DECODER

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
👑 spaCy building blocks and visualizers for Streamlit apps

spacy-streamlit: spaCy building blocks for Streamlit apps This package contains utilities for visualizing spaCy models and building interactive spaCy-

Explosion 620 Dec 29, 2022
Crowd sourced training data for Rasa NLU models

NLU Training Data Crowd-sourced training data for the development and testing of Rasa NLU models. If you're interested in grabbing some data feel free

Rasa 169 Dec 26, 2022
Simple and efficient RevNet-Library with DeepSpeed support

RevLib Simple and efficient RevNet-Library with DeepSpeed support Features Half the constant memory usage and faster than RevNet libraries Less memory

Lucas Nestler 112 Dec 05, 2022
Ongoing research training transformer language models at scale, including: BERT & GPT-2

What is this fork of Megatron-LM and Megatron-DeepSpeed This is a detached fork of https://github.com/microsoft/Megatron-DeepSpeed, which in itself is

BigScience Workshop 316 Jan 03, 2023
A combination of autoregressors and autoencoders using XLNet for sentiment analysis

A combination of autoregressors and autoencoders using XLNet for sentiment analysis Abstract In this paper sentiment analysis has been performed in or

James Zaridis 2 Nov 20, 2021
File-based TF-IDF: Calculates keywords in a document, using a word corpus.

File-based TF-IDF Calculates keywords in a document, using a word corpus. Why? Because I found myself with hundreds of plain text files, with no way t

Jakob Lindskog 1 Feb 11, 2022
Multilingual word vectors in 78 languages

Aligning the fastText vectors of 78 languages Facebook recently open-sourced word vectors in 89 languages. However these vectors are monolingual; mean

Babylon Health 1.2k Dec 17, 2022
CoSENT、STS、SentenceBERT

CoSENT_Pytorch 比Sentence-BERT更有效的句向量方案

102 Dec 07, 2022
Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products

Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products.

Leah Pathan Khan 2 Jan 12, 2022
This is a project built for FALLABOUT2021 event under SRMMIC, This project deals with NLP poetry generation.

FALLABOUT-SRMMIC 21 POETRY-GENERATION HINGLISH DESCRIPTION We have developed a NLP(natural language processing) model which automatically generates a

7 Sep 28, 2021
We have built a Voice based Personal Assistant for people to access files hands free in their device using natural language processing.

Voice Based Personal Assistant We have built a Voice based Personal Assistant for people to access files hands free in their device using natural lang

Rushabh 2 Nov 13, 2021
ProtFeat is protein feature extraction tool that utilizes POSSUM and iFeature.

Description: ProtFeat is designed to extract the protein features by employing POSSUM and iFeature python-based tools. ProtFeat includes a total of 39

GOKHAN OZSARI 5 Dec 16, 2022
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Jan 03, 2023
The training code for the 4th place model at MDX 2021 leaderboard A.

The training code for the 4th place model at MDX 2021 leaderboard A.

Chin-Yun Yu 32 Dec 18, 2022
Natural Language Processing

NLP Natural Language Processing apps Multilingual_NLP.py start #This script is demonstartion of Mul

Ritesh Sharma 1 Oct 31, 2021
This is a simple item2vec implementation using gensim for recbole

recbole-item2vec-model This is a simple item2vec implementation using gensim for recbole( https://recbole.io ) Usage When you want to run experiment f

Yusuke Fukasawa 2 Oct 06, 2022
Codes for processing meeting summarization datasets AMI and ICSI.

Meeting Summarization Dataset Meeting plays an essential part in our daily life, which allows us to share information and collaborate with others. Wit

xcfeng 39 Dec 14, 2022
Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation This repository is the pytorch implementation of our paper: Hierarchical Cr

44 Jan 06, 2023
Simple multilingual lemmatizer for Python, especially useful for speed and efficiency

Simplemma: a simple multilingual lemmatizer for Python Purpose Lemmatization is the process of grouping together the inflected forms of a word so they

Adrien Barbaresi 70 Dec 29, 2022
Production First and Production Ready End-to-End Keyword Spotting Toolkit

Production First and Production Ready End-to-End Keyword Spotting Toolkit

223 Jan 02, 2023