Marlin adapted for polargraph vertical plotting robots. Defaults to dimensions for Makelangelo 5

Unpaired line drawing generation

KoAlpaca: Korean Alpaca Model based on Stanford Alpaca (feat. LLAMA and Polyglot-ko)

🚀 딥러닝 기반 차량 번호 인식 엔진

bert를 이용한 한국어 띄어쓰기

Unofficial implemention of lanenet model for real time lane detection using deep neural network model https://maybeshewill-cv.github.io/lanenet-lane-detection/

Video anonymization by face detection

국립국어원 신문 말뭉치(Ver.1.1) 데이터셋을 활용한 한글뉴스기사 주제분류AI

This work is an updated implementation of LPRNet for Chilean License Plates, which is an end-to-end method for Automatic License Plate Recognition without preliminary character segmentation.

High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Generate Synthetic Korea License Plate.

A python program which performs an FFT on an audio file and produces a MIDI file from the results.

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow

YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )

License Plate Detection and Recognition in Unconstrained Scenarios

Car Recognition with Deep Learning

Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow

A Tensorflow model for text recognition (CNN + seq2seq with visual attention) available as a Python package and compatible with Google Cloud ML Engine.

Implementation of EfficientNet model. Keras and TensorFlow Keras.

🎨 Color recognition both on a webcam stream in real-time, on video and on a single image using K-Nearest Neighbors Machine Learning classification algorithm is trained with Color Histogram Features.

Keras implementation of a CNN network for age and gender estimation

transliterate from english to korean, using seq2seq with attention

A Pure-Python Real-Time Audio Library

Audio fingerprinting and recognition in Python