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/xdever/RFCN-tensorflow
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/xggIoU/centernet_tensorflow_wilderface_voc
https://github.com/xiamenwcy/extended-caffe
https://github.com/xiaogangLi/tensorflow-Darknet53-YOLOv3
https://github.com/xiaohu2015/swint_detectron2
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/xiaopengsu/CenterNet
https://github.com/xiaoxd16/Palm
https://github.com/xiaoxu1025/r-fcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/xiaoyongzhu/Deformable-ConvNets
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/ximilar-com/xcenternet
https://github.com/xinpingwang/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/xinshasha/YoloV3
https://github.com/xinwei666/mmgenerativeir
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/xiuqhou/relation-detr
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/xiuyu0000/new_papers_codes/tree/main/tracktor%2B%2B
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/xiuyu0000/papers_with_examples/tree/main/detr/
https://github.com/xiuyu0000/papers_with_examples/tree/main/maskrcnn
https://github.com/xiuyu0000/tutorials/tree/main/yolov3
https://github.com/xiuyu0000/tutorials/tree/main/yolov4
https://github.com/xiyie/yolox
https://github.com/xjnpark/ds
https://github.com/xolbynz/scaled_yolo
https://github.com/xuanloct4/darknet
https://github.com/xuannianz/EfficientDet
https://github.com/xuannianz/keras-CenterNet
https://github.com/xuantruongdang/module-yolo
https://github.com/xuanyuzhou98/SqueezeSeg
https://github.com/xunhen/py-faster-rcnn-wjc
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/xxnote/stunning-doodle
https://github.com/xytpai/retinanet
https://github.com/xyuan-wyze/darknet
https://github.com/xzabg/faster-rcnn-with-Caltech
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/xzgz/faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/yakhyo/YOLOv1-pt
https://github.com/yan-roo/SpineNet-Pytorch
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.