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/chuongngd/Images-Object-Detection
https://github.com/citbrains/darknet_yolov4
https://github.com/cjvargasc/YOLOv2
https://github.com/clasikman/darknet
https://github.com/clemkoa/faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/clemkoa/fasterrcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/clive819/Modified-DETR
https://github.com/cloudnine148/PHC_2nd_SPC
https://github.com/cmf3673/spotti
https://github.com/code-implementation1/Code1/tree/main/EfficientDet_d0
https://github.com/code-implementation1/Code5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code6/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/code-implementation1/Code6/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/code-implementation1/Code6/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/code-implementation1/Code7/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/code-implementation1/Code7/tree/main/res2net_yolov3
https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code7/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/code-implementation1/Code8/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1
https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_resnet50
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/code-implementation1/Code9/tree/main/SwinTransformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/code-implementation1/Code9/tree/main/YOLOX
https://github.com/code-implementation1/Code9/tree/main/YOLOv3
https://github.com/code-implementation1/Code9/tree/main/YOLOv4
https://github.com/cokowpublic/Nuclei-Segmentation
https://github.com/collectionslab/Omniscribe
https://github.com/collectionslab/annotations-computervision
https://github.com/collectionslab/book-annotation-classification
https://github.com/coreqode/HardNet
https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
https://github.com/csm-kr/Retinanet_pytorch
https://github.com/csmiler/tf-faster-rcnn-cpu
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/cszzshi/SimD
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/cuihu1998/EfficientDet-d0
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.