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Ardian Umam's Projects

atlasnetv2 icon atlasnetv2

This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

banknotesrecognition icon banknotesrecognition

Repository for our VCIP 2018 paper, 'A Light Deep Learning Based Method for Bank Serial Number Recognition'. It contains all the dataset with the code.

compilations icon compilations

This repository contains compilation of some chosen topics.

dl-2020-final-project icon dl-2020-final-project

This is the final project for the Deep Learning class, NCTU - Fall 2020. The objective is to build a robot to win a line-following competition. Our team won first place out of 10 teams.

kalman-filters icon kalman-filters

Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.

larapus icon larapus

Coba belajar Larapus dari bukunya Mas Rahmad Awaludin

map icon map

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

models icon models

Models and examples built with TensorFlow

mybook icon mybook

This repository is for all codes and data related to my book.

pointmixswap icon pointmixswap

This is the official repository of our ECCV 2022 paper, "Point MixSwap: Attentional Point Cloud Mixing via Swapping Matched Structural Divisions".

setvae icon setvae

[CVPR'21] SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

tensorflow-tensorrt icon tensorflow-tensorrt

This repository is for my YT video series about optimizing a Tensorflow deep learning model using TensorRT. We demonstrate optimizing LeNet-like model and YOLOv3 model, and get 3.7x and 1.5x faster for the former and the latter, respectively, compared to the original models.

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