https://github.com/yang-0201/YOLOv6_pro
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
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/yang-233/waste_detect
https://github.com/yangdashi888/CenterNet-windows
https://github.com/yangkunpeng-coder/EfficientDet_libtorch
https://github.com/yangyangxu0/demt
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/yangyucheng000/DETR
https://github.com/yangyucheng000/Mask-RCNN
https://github.com/yangyucheng000/University/tree/main/model-3/swin
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/yangyucheng000/ascend_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/yangyucheng000/convnext/blob/main/convnext.py
https://github.com/yangyucheng000/maskrcnn_mobilenetv1
https://github.com/yangyucheng000/mnasnet/blob/main/mnasnet.py
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/yanxp/rcnn-discovery
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/yaoming0712/dalinmask
https://github.com/yasarniyazoglu/YOLOR-----Object-Detection-in-Custom-Data-with-YoloR
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/yashkarbhari/YOLOv4_ObjectDetection_tf2
https://github.com/yaya-yns/tart
https://github.com/ycchiusieve/yolo3
https://github.com/yczhang1017/SSD_resnet_pytorch
https://github.com/yeahkun/caffe-yolo
https://github.com/yet124/yolov3-pytorch
https://github.com/yfnn/fused-model
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/yfnn/person_subclass
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/yhenon/pytorch-retinanet
https://github.com/yijiaceline/Final-Project-Group4
https://github.com/yingkaisha/keras-vision-transformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/yiningzeng/darknet-fork-from-AlexeyAB
https://github.com/yj-littlesky/py-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/ykasten/layered-neural-atlases
https://github.com/yktangac/CenterHarDNet
https://github.com/ylxiao88/CenterNet
https://github.com/yoojy31/mixture-density-object-detector
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
https://github.com/yoshitomo-matsubara/sc2-benchmark
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
https://github.com/you-leee/deep-nn-examples
https://github.com/youchangxin/YOLOv4_tensorflow2
https://github.com/youngwanLEE/VoVNet-RefineDet
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.