https://github.com/facebookresearch/hiera
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.
This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.
<span style="color:grey; opacity: 0.6">( Image credit: CenterNet )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Deformable DETR: Deformable Transformers for End-to-End Object Detection
R-FCN: Object Detection via Region-based Fully Convolutional Networks
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
Pelee: A Real-Time Object Detection System on Mobile Devices
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Contour Proposal Networks for Biomedical Instance Segmentation
R-FCN: Object Detection via Region-based Fully Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.