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/AbhinavJhanwar/face_detection
https://github.com/AbhinavJhanwar/object-detection-classification
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Adam123wu/Edge-YOLO_algorithm
https://github.com/AdeelH/pytorch-multi-class-focal-loss
https://github.com/AdolfoMatias/rede_neural_laranja_maca_adolfo
https://github.com/Ahsanr312/Object-Detection-and-Tracking-using-YOLOv3-and-DeepSort
https://github.com/Ahsanr312/Object-Detection-in-Satellite-Imagery-Using-YOLOv3-
https://github.com/AjJordy/squeezeDet-master
https://github.com/AkashGanesan/PedestrianAttention
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/AlassaneSakande/A-ConvNet-of-2020s
https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection
Robust and Efficient Post-Processing for Video Object Detection (REPP)
https://github.com/Alexander-Whelan/Zeus
https://github.com/AlongRide/CenterNet_anchor_free
https://github.com/AlphaJia/pytorch-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/AlphaJia/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/AmeerHamza111/yolo-darknet
https://github.com/AmitNativ1984/yolov3
https://github.com/AndreydeAguiarSalvi/yolo_compression
https://github.com/AntixK/PyTorch-Model-Compare
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Arno3165229/CornerNet_Traffic_Light
https://github.com/ArtLabss/tennis-tracking
https://github.com/Arthur-Shi/py-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Ashish-Sankritya/darknet_CNN
https://github.com/AshishSingh2261/Pedestrian_Instance_Segmentation
https://github.com/Asthestarsfalll/ConvNeXt-MegEngine
https://github.com/AtlasCoCo/Darknet
https://github.com/Atom-min/MLBigWork
https://github.com/BBuf/Darknet
https://github.com/BR-IDL/PaddleViT
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/BR-IDL/PaddleViT/blob/main/object_detection/DETR
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt
https://github.com/Baibaifan/An-Improved-PeleeNet-Algorithm-with-Feature-Pyramid-Networks-for-Image-Detection-
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Banus/caffe-demo
https://github.com/BathVisArtData/PeopleArt
https://github.com/Beaver48/kaggle-chest-xray-abnormalities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.