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UniPercepNet-S
June 10, 2025 – Present
UniPercepNet-S, a lightweight dual-task framework designed for real-time object detection and instance segmentation. Built on a YOLOF-style one-level detector, the model employs a dilated-convolution neck to enlarge receptive fields without relying on feature pyramid network (FPN)
View ProjectYOLOv2_from_scratch
January 29, 2024 – July 23, 2024
While reading YOLOV2, have you ever considered how anchor boxes work and how to define them? And other repositories out there are too complicated to comprehend. This repository may be an answer to your questions :>>
View ProjectClassification_pytorch
July 29, 2022 – December 23, 2022
Classification_pytorch — GitHub repository
View ProjectSartorius-Cell-Instance-Segmentation
June 29, 2022 – Present
Using Cellpose model and customized Mosaic algorithm to deal with cell instance segmentation task
View Projectstationery-store
November 5, 2021 – January 12, 2022
stationery-store — GitHub repository
View ProjectYOLOv1_from_scratch
September 16, 2021 – July 23, 2024
Implementation of paper - YOLOv1: You Only Look Once: Unified, Real-Time Object Detection
View Projectread-noti-on-tdtu
September 6, 2020 – August 2, 2021
Automatically read notifications on TDTU webpage
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily focused on personal, academic-style projects in computer vision and deep learning. While this demonstrates strong technical interest and initiative, there is no information regarding experience in collaborative environments, diverse project types beyond research implementations, or contributions to open-source projects that would indicate cultural fit within a typical industry setting. The projects are highly aligned with a Data Scientist role specializing in computer vision.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's profile primarily showcases technical project work without details on collaboration, problem-solving approaches, or communication in a team setting.