Detection3 [X:AI] YOLOv4 논문 리뷰 논문 원본 : https://arxiv.org/abs/2004.10934 YOLOv4: Optimal Speed and Accuracy of Object DetectionThere are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operatearxiv.org 1. Introduction고속 고정확도의 객체 탐지기를 일반적인 .. 2025. 7. 9. [X:AI] RetinaNet 논문 리뷰 논문 원본 : https://arxiv.org/abs/1708.02002 Focal Loss for Dense Object DetectionThe highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense samplarxiv.org 1. Abstract & Introduction본 논문 시점 기준 Object Detection .. 2025. 2. 3. [X:AI] YOLO 논문 리뷰 논문 원본 : https://arxiv.org/abs/1506.02640발표 영상 : https://www.youtube.com/watch?v=WDX0PtWrRcw You Only Look Once: Unified, Real-Time Object DetectionWe present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class prob.. 2025. 1. 28. 이전 1 다음
