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/muddge/yolov3-person-only
https://github.com/mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/multimodallearning/pytorch-mask-rcnn
https://github.com/munachisonwadike/YOLOV3
https://github.com/muraliadithya/vdp
https://github.com/murdockhou/Yet-Another-EfficientDet-Pytorch-Convert-ONNX-TVM
https://github.com/murufeng/awesome_lightweight_networks
https://github.com/mutichung/yolov3_pt
https://github.com/muyiguangda/pytorch-yolov3
https://github.com/muyistarsky/MaskRCNN
https://github.com/mvxxx/mimuw-hats
https://github.com/mzeromiko/vmamba
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/nabarunbaruaAIML/ATCC_Yolov5
https://github.com/namdvt/Focal-loss-pytorch-implementation
https://github.com/nataliele/waldo
https://github.com/natanielruiz/android-yolo
https://github.com/nathanlem1/igae-net
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/native2019/hello
https://github.com/nautilus261/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/naviocean/CenterNet
https://github.com/necla-ml/Deformable-ConvNets-py3
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/neilctwu/FCOS-pytorch_Simplified
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/nemonameless/PaddleDetection_YOLOSeries
https://github.com/nemonameless/PaddleDetection_YOLOX
https://github.com/neshitov/UNet
https://github.com/neung678/anti_bird
https://github.com/neuqgz/modify-faster-rcnn-tf
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/neverleave0916/darknet
https://github.com/newsun-boki/yolovx-openvino-c--video-infer
https://github.com/newsun-boki/yolox-openvino-video-infer
https://github.com/nghgphi/fire_detection
https://github.com/ngrayluna/NotCake
https://github.com/nguoido/Yolo-alexeyAB
https://github.com/nikhithakarennagari/1311
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/nikhithakarennagari/Git
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.