jinseol12159988

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

실습 중심

ai in practice

AI 활용법

ai 코딩

AI 코딩

yolo

detr

YoungJea Oh

입문

초급

중급이상

파이토치

PyTorch

Computer Vision(CV)

합성곱신경망

Preparing the Learning Environment

Computer Vision / CNN Fundamentals

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

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

Transformer-based Object Detection - DETR (Detection with Transformers)