I found a lecture I really needed after such a long time
Rather than various complex and difficult mathematical perspectives, showing it code-centered is refreshing. I can quickly learn what CNN is and how to utilize it
The months I spent looking at various books and online lectures feel like such a waste
As a bonus, if you had simply included image labeling work, etc., I think this would be the best vision lecture for many people struggling with vision

lovesome994824

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

2025 추석맞이 감사할인

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

Thank you for the positive feedback. I will also consider the advice you provided for the next course update.

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)