Papers with Code 아카이브
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/nilesh0109/PedestrianTracking
https://github.com/nlee-208/face_mask_detection
https://github.com/nmcasasr/darknet2
https://github.com/nmcasasr/darknet_oldVersion
https://github.com/noelcodes/Mask_RCNN
https://github.com/noelcodes/YOLO
https://github.com/noelcodes/cloth-recognition-version2
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/nsarang/MnasNet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/nsoul97/yolov1_pytorch
https://github.com/nuaaceieyty/Paddle-YOLOv2
https://github.com/nuaaceieyty/Paddle-YOLOv4
https://github.com/nuoaier/BaiduStar2020-18-Competition-Solution
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/nurulxakmar/darknet
https://github.com/nweakly/MSDSProject-II
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/oaqoe-DWQ/Yolov5_DeepSort_Pytorch
https://github.com/object-detection-algorithm/RPN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/ogatarina-sq/secyolov7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/okboseong/darknet
https://github.com/oki5656/Estimating_Distance
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://github.com/omni-us/squeezedet-keras
https://github.com/oorrppp2/darknet-pose
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmclassification
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/open-mmlab/mmdetection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/open-mmlab/mmpose
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/open-mmlab/mmrotate
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/open-mmlab/mmyolo
https://github.com/osmr/imgclsmob
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/otamajakusi/darknet-yolov4
https://github.com/oulin1031esti/detect
Training-Time-Friendly Network for Real-Time Object Detection
https://github.com/oyeabhijit/yolo-obj-detection
https://github.com/ozora-ogino/efficient_backbones
https://github.com/paradisetechsoftsolutions/Deep-learning-model-using-Mask-R-CNN-for-predicting-AC-remote
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.