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/wandahangfy/yolov11-rgbt
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
https://github.com/wangdongdut/dut-anti-uav
https://github.com/wangermeng2021/EfficientDet-tensorflow2
https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2
https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2
https://github.com/wanglaotou/centernet_ncnn
https://github.com/wangxianhao/Improved-YOLOv3-for-UAV
https://github.com/wangzpeng/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/waspinator/deep-learning-explorer
https://github.com/waterdisappear/nudt4mstar
https://github.com/wavce/efficientdet-tf2
https://github.com/wayne1204/NOAA-fish-finding
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/wdlctc/darknet
https://github.com/weidalin/yolov4_mixup
https://github.com/weiwang31/icemamba
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/wennieWN/endernewton_tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/westerndigitalcorporation/YOLOv3-in-PyTorch
https://github.com/wiegehtki/nvjetson_opencv_gsi
https://github.com/wiegehtki/zoneminder
https://github.com/wiegehtki/zoneminder-jetson
https://github.com/williamccondori/YOLO-NFPA
https://github.com/wincelinux/ai-dn-FasterRCNN-caffe
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/witwickey/alexeyDarknet
https://github.com/witwickey/darknet
https://github.com/wodyjowski/colab-training
https://github.com/wongkinyiu/yolov9
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
https://github.com/wonseok-Jang/ros_darknet
https://github.com/wonseok-Jang/rtod_darknet
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving
https://github.com/wpf535236337/pytorch-peleenet
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/wrappr/wrappr-core
https://github.com/wslerry/yolov5
https://github.com/wsmlby/darknetnano
https://github.com/wuyx/pva-faster-rcnn
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/wuzhihao7788/yolodet-pytorch
https://github.com/xTRam1/Object-Detection-on-Custom-Dataset
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.