Jalilnkh
یازیچی تۆرکجه کیتاب اوخویان Biometric researcher, Deep learner
Omniaz: Augmented Reality Shopping SolutionsPoland
Pinned Repositories
FastAi-Flask-SQLAlchemy
In this repository, I would add the written codes using FastAI, Flask and SQLAlchemy
Jalilnkh
KartalOl-Fluent-Python-2022
I am going to read Fluent Python SECOND EDITION Clear, Concise, and Effective Programming and publish what I am learning from new version of the book.
KartalOl-NIR-ISL2021031301
Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KaratolOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KaratolOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301 .
Kubernates-for-learning
I have get from my boss a video entitled "Kubernetes Course - Full Beginners Tutorial (Containerize Your Apps!)"
LASER
Language-Agnostic SEntence Representations
MIT-6.00.2x-Introduction-to-Computational-Thinking-and-Data-Science
I going to learn about MIT 6.00.2x Introduction to Computational Thinking and Data Science, therefor if anyone thinks that could join here and read what can learn quickly or what can add here wellcome and let's do it.
MU-Net
U-Net Model for Sclera Segmentation in the Mobile Environment using a Transfer Learning approach on MobileNetV2
Applications-for-Turk-Languages
In this repository we will create applications for Iranian Turk people. Join us and let us know how can you help us in this road.
shap
A game theoretic approach to explain the output of any machine learning model.
Jalilnkh's Repositories
Jalilnkh/KartalOl-Fluent-Python-2022
I am going to read Fluent Python SECOND EDITION Clear, Concise, and Effective Programming and publish what I am learning from new version of the book.
Jalilnkh/KartalOl-NIR-ISL2021031301
Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KaratolOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KaratolOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301 .
Jalilnkh/MU-Net
U-Net Model for Sclera Segmentation in the Mobile Environment using a Transfer Learning approach on MobileNetV2
Jalilnkh/Jalilnkh
Jalilnkh/Kubernates-for-learning
I have get from my boss a video entitled "Kubernetes Course - Full Beginners Tutorial (Containerize Your Apps!)"
Jalilnkh/FastAi-Flask-SQLAlchemy
In this repository, I would add the written codes using FastAI, Flask and SQLAlchemy
Jalilnkh/LASER
Language-Agnostic SEntence Representations
Jalilnkh/MIT-6.00.2x-Introduction-to-Computational-Thinking-and-Data-Science
I going to learn about MIT 6.00.2x Introduction to Computational Thinking and Data Science, therefor if anyone thinks that could join here and read what can learn quickly or what can add here wellcome and let's do it.