Real-Time Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 8
- 보존된 평가 기록
- 99
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-1/ms-code-182
https://github.com/2023-MindSpore-1/ms-code-186
https://github.com/2023-MindSpore-1/ms-code-191
https://github.com/2023-MindSpore-1/ms-code-194
https://github.com/2023-MindSpore-1/ms-code-202
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-207
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-21
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/2023-MindSpore-1/ms-code-212/tree/main/EfficientDet_d0
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_dcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_ssod
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/hardnet
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_yolov3
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinydarknet
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet
https://github.com/2023-MindSpore-1/ms-code-31
https://github.com/2023-MindSpore-1/ms-code-39
https://github.com/2023-MindSpore-1/ms-code-44
https://github.com/2023-MindSpore-1/ms-code-51
https://github.com/2023-MindSpore-1/ms-code-59
https://github.com/2023-MindSpore-1/ms-code-60
https://github.com/2023-MindSpore-1/ms-code-69
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tinydarknet
https://github.com/2023-MindSpore-4/Code-5/tree/main/hardnet
https://github.com/2023-MindSpore-4/Code-5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/2023-MindSpore-4/Code-5/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/2023-MindSpore-4/Code10/tree/main/FasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-4/Code10/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaNet
https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOX
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.