I am a university student studying in a computer vision-related department. The lectures were meticulous, and above all, they helped me greatly by explaining in detail so that no ambiguous parts remained. While taking the course, I became interested in other lectures as well. However, it is true that the price range of the lectures is somewhat high, making it burdensome for me as a student. Regarding videos, it would be even better if there were additional updates on various computer vision technologies beyond object recognition. I will continue to diligently take the remaining lectures. Thank you for creating such excellent lectures.

jyj1206

This course is a step-by-step learning process that covers the principles of image and object recognition using deep learning, from fundamentals to the latest models.
- Building Foundations: Understanding tensors and basic neural network structures with PyTorch
- Understanding Images: Learning computer vision concepts, image data structures, and augmentation techniques
- CNN Model Training: Hands-on image classification practice with Convolutional Neural Networks (CNN) using datasets like CIFAR-10
- Transfer Learning: Leveraging pre-trained models to achieve fast learning with limited data
- Object Detection: Understanding and practicing with the latest object detection models including R-CNN, YOLO, SSD, DETR
- Segmentation: Experiencing pixel-level object segmentation through U-Net and Mask R-CNN

trimurti

Deep Learning-Based Image and Object Recognition: From CNN to YOLO and DETR

Thank you for the good review. If you're facing financial burden as a student, please let me know which lecture you'd like to watch and I'll send you a discount coupon.

실습 중심

ai in practice

AI 활용법

ai 코딩

AI 코딩

yolo

detr

이 강의는 딥러닝을 활용한 이미지와 객체 인식의 원리를 기초부터 최신 모델까지 단계적으로 배우는 과정입니다.
- 기초 다지기: 파이토치(Pytorch)로 텐서와 신경망 기본 구조 이해
- 이미지 이해하기: 컴퓨터 비전의 개념, 이미지 데이터 구조, 증강(Augmentation) 기법 학습
- CNN 모델 학습: 합성곱 신경망(CNN)으로 이미지 분류 실습 (CIFAR-10 등)
- 전이 학습(Transfer Learning): 기존 학습된 모델을 활용해 적은 데이터로 빠르게 학습
- 객체 탐지(Object Detection): R-CNN, YOLO, SSD, DETR 등 최신 객체 탐지 모델 이해 및 실습
- 세그멘테이션(Segmentation): U-Net, Mask R-CNN을 통한 픽셀 단위 객체 분할 경험

YoungJea Oh

입문

초급

중급이상

파이토치

PyTorch

Computer Vision(CV)

컴퓨터 비전

합성곱신경망

딥러닝 기반 이미지·객체 인식: CNN에서 YOLO·DETR까지

교육환경 준비

컴퓨터 비전 (Computer Vision) / CNN 기초

객체 탐지/분할(Object Detection/Segmentation) - Faster R-CNN / Mask R-CNN

객체 탐지/분할(Object Detection/Segmentation) - YOLO, SSD, U-Net

Transformer 기반 객체 탐지 - DETR (Detection with Transformers)