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

Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

Open-source offline translation library written in Python. Uses OpenNMT for translations

Multilingual finetuning of Machine Translation model on low-resource languages. Project for Deep Natural Language Processing course.

Practical Machine Learning with Python

Sample data associated with the Aurora-BP study

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

[ICLR 2021 Spotlight] Pytorch implementation for "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts."

Legal text retrieval for python

Application for shadowing Chinese.

🦆 Contextually-keyed word vectors

Finetune gpt-2 in google colab

Data loaders and abstractions for text and NLP

Learning Spatio-Temporal Transformer for Visual Tracking

Host your own GPT-3 Discord bot

🦅 Pretrained BigBird Model for Korean (up to 4096 tokens)

Finally, some decent sample sentences

AudioCLIP Extending CLIP to Image, Text and Audio

Speach Recognitions

nlp-tutorial is a tutorial for who is studying NLP(Natural Language Processing) using Pytorch

Data preprocessing rosetta parser for python