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/lixiaolei1982/DenseNet-base-Mask-RCNN-for-Human-Pose-Estimation
https://github.com/liyemei/Deformable-active-convolutional
https://github.com/llsouder/screen-ocr
https://github.com/lmeulen/PeopleCounter
https://github.com/loadbxh/yolov3
https://github.com/lobznet/yolov3
https://github.com/long95288/car-logo-detect
https://github.com/longbai1006/surgical-vqla
https://github.com/longcw/roialign.pytorch
https://github.com/longcw/yolo2-pytorch
https://github.com/lonnyzhang423/github-hot-hub
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/louisyuzhe/car-damage-detector
https://github.com/lrf008/yolov4
https://github.com/ls5122/mask-rcnn
https://github.com/lucasjinreal/yolov7_d2
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/lucidrains/denoising-diffusion-pytorch
https://github.com/lucifer443/SpineNet-Pytorch
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/luckymanwu/defect_detection
https://github.com/lucylow/salty-wet-man
https://github.com/lukewenMX/RCNN_simple_detection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/lukoucky/image_recommendation
https://github.com/lvweiwolf/efficientdet
https://github.com/lyffly/YOLOv3_with_my_Dataset
https://github.com/lyk19940625/MyRFBNet
https://github.com/lyqcom/aaic_swintransformerv2
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/lyqcom/convnext
https://github.com/lyqcom/detr
Deformable DETR: Deformable Transformers for End-to-End Object Detection
https://github.com/lyqcom/efficientdet
https://github.com/lyqcom/fasterrcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/lyqcom/fasterrcnn-fpn-dcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/machine-learning-glossary/glossary
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/macomino/TFM
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/madamak/ultrapy-yolov3-chess
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahavir-GPI/yolov3
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.