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/tensorflow/models/tree/master/official/projects/detr
https://github.com/tensorflow/models/tree/master/official/vision
https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo
https://github.com/tensorflow/models/tree/master/research/object_detection
https://github.com/tensorflow/tpu/tree/master/models/official/detection
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/texasinstruments/edgeai-yolov5
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/texasinstruments/edgeai-yolox
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/thanhtran98/yolov3_jetbot
https://github.com/theAIGuysCode/yolov3_deepsort
https://github.com/thecryboy/DECONV
https://github.com/thecryboy/yolov3_ultralytics_deconv
https://github.com/theinmate4587/Autonomous-driving---Car-detection
https://github.com/thekauer/traindarknet
https://github.com/thekoshkina/blackai-challenge
https://github.com/thinhhoang95/helipad-yolo
https://github.com/thodorostsao/4-leg-robot-neural-network
https://github.com/thtrieu/darkflow
https://github.com/tiagoCuervo/JapaNet
https://github.com/tianhai123/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/tianhai123/yolov3
https://github.com/tigerofmurder/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/tkhhhh/yolov3-master
https://github.com/tmbaoloc/darknet
https://github.com/tms2003/yolov3
https://github.com/toandaominh1997/EfficientDet.Pytorch
https://github.com/tommyjtl/darknet-colab
https://github.com/tonyhsu32/AI-Cafe-with-machine-learning
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
https://github.com/tops666/volo-v4-
https://github.com/tossy0423/darknet_ros
https://github.com/totoadel/Minst
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/toufiksk/darknet
https://github.com/towhee-io/towhee
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/travismyers19/Vulnerable_Buildings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.