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/laclouis5/darknet2
https://github.com/lampsonSong/tinySSD
https://github.com/lanPN85/HarDNet-MSEG
https://github.com/lanmei1211/face
https://github.com/layumi/Person_reID_baseline_pytorch
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/lazyn1997/YOLOX
https://github.com/lcxzzz0113/CenterNet
https://github.com/leanderme/ConvNeXt-Tensorflow
https://github.com/leduckhai/Global-Wheat-Detection-Kaggle
https://github.com/lee-man/movenet
https://github.com/leggedrobotics/darknet
https://github.com/leggedrobotics/darknet_ros
https://github.com/leobean/CenterNet_oriented
https://github.com/leochangzliao/OPBM
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/leon-liangwu/MaskYolo_Caffe
https://github.com/leon-liangwu/py-caffe-yolo
https://github.com/leonardhan1979/fasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/convnext
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientdet
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolor
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolox
https://github.com/lhcezx/Graph-FPN
https://github.com/lhwcv/mlsd_pytorch
https://github.com/li-yibing/oceannet
https://github.com/liangheming/faster_rcnnv1
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/lichengunc/mask-faster-rcnn
https://github.com/lichuanqi/YOLOV4_Darknet
https://github.com/lincaiming/py-faster-rcnn-update
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/lincaiming/py-faster-rcnn-windows
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/liuch37/image-processing
https://github.com/liuji93/yolo_v3
https://github.com/liulianjushi/yolo_v3_tf2.0
https://github.com/liuqiang3/faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/liuxiangchao369/VRS-training
https://github.com/liuxingwt/CLS
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.