Real-Time Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 8
- 보존된 평가 기록
- 99
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/CuberrChen/efficientdet-paddle
https://github.com/CycloneBoy/PPDetectionPytorch
https://github.com/DInesh1234-wq/tyredataset
https://github.com/DaloroAT/first_break_picking
https://github.com/Dandelight/VoVNet-PaddlePaddle
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/DarkGeekMS/Pytorch-YOLOv3-Implementation
https://github.com/DarshanDeshpande/jax-models
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/DataXujing/EfficientDet_pytorch
https://github.com/DataXujing/TensorRT-DETR
https://github.com/DataXujing/YOLOR-
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/DataXujing/YOLOX-
https://github.com/DavianYang/yolo.ai
https://github.com/DeNA/Chainer_Mask_R-CNN
https://github.com/DeNA/PyTorch_YOLOv3
https://github.com/Deci-AI/super-gradients
https://github.com/Deep-Learner/Plot_CNNs_Python_Latex
https://github.com/Deeplodocus/COCO-with-YOLO
https://github.com/Demonhesusheng/darknet_v2
https://github.com/DerrickXuNu/OpenCOOD
https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation
https://github.com/DevBruce/YOLO-TF2
https://github.com/DevBruce/YOLOv1-TF2
https://github.com/DevBruce/YOLOv3-TF2
https://github.com/DivJAth/DeepLearning5922
https://github.com/Dodant/ANPR-with-Yolov4
https://github.com/DominickZhang/Distillation-Swin-Transformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/DonCorle0ne/damage_detection
https://github.com/Donvink/Car_Detection_for_Autonomous_Driving
https://github.com/DrMMZ/RetinaNet
https://github.com/EMsnap/RobotSorting
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
https://github.com/Ededu1984/car_detection_forza_horizon
https://github.com/EmGarr/kerod
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Ereebay/CV-Documents
https://github.com/Ereebay/Deep-Learning-Documents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.