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/MindCode-4/code-7/tree/main/isr
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/MindSpore-MS-Code2/code0/tree/main/ESRGAN
https://github.com/MindSpore-MS-Code2/code0/tree/main/EfficientDet_d0
https://github.com/MindSpore-MS-Code2/code0/tree/main/efficientnet-b0
https://github.com/MindSpore-paper-code-2/code2/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/MindSpore-paper-code-2/code2/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-2/code2/tree/main/res2net_yolov3
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet_34
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/MindSpore-paper-code-2/code3/tree/main/tinydarknet
https://github.com/MindSpore-paper-code-2/code3/tree/main/tracktor
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-2/code3/tree/main/yolov3_tiny
https://github.com/MindSpore-paper-code-3/code10/tree/main/Gold_YOLO
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
https://github.com/MindSpore-paper-code-3/code10/tree/main/faster_rcnn_dcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-3/code10/tree/main/hardnet
https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_dcn
https://github.com/MindSpore-paper-code-3/code4/tree/main/hardnet
https://github.com/MindSpore-paper-code-3/code5/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-3/code5/tree/main/res2net_yolov3
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
https://github.com/MindSpore-paper-code-3/code5/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/MindSpore-paper-code-3/code7/tree/main/faster_rcnn_dcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/EfficientDet_d0
https://github.com/MindSpore-paper-code-3/code8/tree/main/cspdarknet53
https://github.com/MindSpore-paper-code-3/code9/tree/main/Efficientnet
https://github.com/MindSpore-paper-code-3/code9/tree/main/FasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN
https://github.com/MindSpore-scientific/code-7/tree/main/YOLOS
https://github.com/MohamedAbd0/darkflow-training
https://github.com/Mohammad-Asad-110/Fashion-Recommendation
https://github.com/MonsterPeng/Deformable-ConvNets-master
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Mrnoorsingh/car-parking
https://github.com/Myyyr/transseg2d
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/NBvinay/vinb-ghost-car
https://github.com/NEUAutonomousDriving408/CIVCAutonomousDriving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.