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Регистрация: 02.01.2024

Dmitry Shkadarevich

Специализация: Computer Vision Engineer

Портфолио

AR Camera

• Built C++ framework for AR camera. Converted and Deployed on IOS. • Deployed method to render 3D masks on a face via OpenGL. • Applied Face Recognition/Tracking, Selfie Segmentation, Face Beautification. • Perform camera undistortion, worked closely with AVFoundation. • Developed Qt-applications for internal needs in computer vision team. • Reimplement core computer vision algorithms on GPU (Metal,OpenGL ES).

Snapmole

• Applied smart feature-fusing for RGB+Depth inputs from IPhone camera. • Researched and reproduced sota approaches. Trained MobileVitV2 on custom dataset and wrapped it to run inference on IOS. Trained segmentation model for masking skin leasions. • Managed AWS instance with 8 GPU to run PyTorch Distributed Data-Parallel Training. • Worked on dataset collection and annotation. Managed annotation team of 10 dermatologist.

Nufa

• Diffusion models research and development for AI avatars generation. • Image2Image, Text2Image, Adapters, LoRAs training. • Develop post-processing algorithms for smooth body change inpainting. • Develop smart tattoo generator, based on SD Inpaint. • Conduct research on Virtual TryOn. • Work on dataset gathering and filtering. Annotation team management.

Скиллы

Docker
FFmpeg
Git
Gstreamer
Jira
Keras
Linux
Markdown
Mediapipe
Numpy
Objective-C
OpenCV
OpenGL ES
OpenVINO
Pandas
Python
PyTorch
Scikit-learn
Swift
TensorBoard
TensorFlow
Trello
С++

Опыт работы

ML Advisor
с 09.2023 - По настоящий момент |NDA
Cloud/Static GPU
• Interview and manage core development team. • Provide initial road-map for skin cancer detection device. • Consult on Cloud/Static GPU solutions.
ML Advisor
04.2023 - 09.2023 |NDA
Retail, MVP, Fast api, LAMA
• Designed a pipeline for automatic 2D virtual try-on solution(Retail). • Assisted CEO in the creation of the Minimum Viable Product (MVP), from idea to a production. • Designed and implemented a system architecture for a web-based ML application(Fast api). • Deployed models for Background removal(Salient object detection) and Image Inpainting(LAMA).
Computer Vision Engineer
с 04.2023 - По настоящий момент |Nufa
Image2Image, Text2Image, LoRAs, SD
• Diffusion models research and development for AI avatars generation. • Image2Image, Text2Image, Adapters, LoRAs training. • Develop post-processing algorithms for smooth body change inpainting. • Develop smart tattoo generator, based on SD Inpaint. • Conduct research on Virtual TryOn. • Work on dataset gathering and filtering. Annotation team management.
Computer Vision Engineer
05.2022 - 04.2023 |Snapmole
RGB+Depth, MobileVitV2, AWS
• Applied smart feature-fusing for RGB+Depth inputs from IPhone camera. • Researched and reproduced sota approaches. Trained MobileVitV2 on custom dataset and wrapped it to run inference on IOS. Trained segmentation model for masking skin leasions. • Managed AWS instance with 8 GPU to run PyTorch Distributed Data-Parallel Training. • Worked on dataset collection and annotation. Managed annotation team of 10 dermatologist.
Computer Vision Engineer
09.2021 - 05.2022 |AR Camera
C++, OpenGL, GPU, Metal, OpenGL ES
• Built C++ framework for AR camera. Converted and Deployed on IOS. • Deployed method to render 3D masks on a face via OpenGL. • Applied Face Recognition/Tracking, Selfie Segmentation, Face Beautification. • Perform camera undistortion, worked closely with AVFoundation. • Developed Qt-applications for internal needs in computer vision team. • Reimplement core computer vision algorithms on GPU (Metal,OpenGL ES).
Machine Learning Engineer
09.2020 - 09.2021 |NDA
C++, OpenCV, Google’s Mediapipe
• Developed a pipeline to automatically perform product recognition and tracking in the smart fridge. • Built C++ solution on top of the Google’s Mediapipe framework. Deployed on NVIDIA® Jetson Nano. • Utilized OpenCV, Gstreamer/Deepstream and FFmpeg to process images and video streams. • Applied panoramic image stitching, hand tracking, motion saliency detection, feature points detection and tracking using only classic computer vision algorithms. Trained tinyYOLOv4 for product detection.

Образование

Faculty of Computer Systems and Networks (Бакалавр)
2018 - 2022
Belarusian State University of Informatics and Radioelectronics

Языки

АнглийскийВыше среднегоФранцузскийБазовыйБелорусскийРоднойРусскийРодной