Real-Time Object Detection
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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-4/Code10/tree/main/YOLOv5
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet152
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/2023-MindSpore-4/Code11/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_yolov3
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/2023-MindSpore-4/Code3/tree/main/EfficientDet_d0
https://github.com/2023-MindSpore-4/Code6/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/2023-MindSpore-4/Code8/tree/main/convnext
https://github.com/2023-MindSpore-4/Code9/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code9/tree/main/YOLOv3
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/convnext
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/detr
https://github.com/2024-MindSpore-1/Code4/tree/main/Efficientnet
https://github.com/2024-MindSpore-1/Code4/tree/main/FasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet
https://github.com/2anchao/VovJpu
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/3epochs/you-only-look-once
https://github.com/497626895/darknet
https://github.com/593903762/center
https://github.com/593903762/ttf
Training-Time-Friendly Network for Real-Time Object Detection
https://github.com/6-dl/darknet_wpb
https://github.com/97andrea97/CenterNet-PNPDet
https://github.com/AGroupofProbiotocs/YOLO_V2_in_Tensorflow
https://github.com/AKASH2907/bird-species-classification
https://github.com/AKASH2907/bird_species_classification
https://github.com/Aarav-Patel/darknet
https://github.com/Abdulrahman-Adel/Object-Detection
https://github.com/Abhi-899/YOLOV4-Custom-Object-Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.