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/patelmiteshn/darknet
https://github.com/patrick013/object-detection---yolov3
https://github.com/pawelbeza/ClothesDetectorModel
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/pengrenlai/RetinaNet-tensorflow
https://github.com/peter8301120/darknet_t
https://github.com/petrum01/Capstone_project_object_detection
https://github.com/phnk/yolov3
https://github.com/phtruongan/py-faster-rcnn-docker
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/phykn/film-defect-detection
https://github.com/piaosonglin1985/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/pierluigiferrari/ssd_keras
https://github.com/pistachio0812/YOLOX
https://github.com/pixiedust18/darknet_mask_classification
https://github.com/pj1920/mask-r-cnn
https://github.com/pjreddie/darknet
https://github.com/playerkk/face-py-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/pminhtam/keras_yolo3
https://github.com/pmj110119/yolox
https://github.com/polospeter/TensorFlow-Advanced-Techniques-Specialization
https://github.com/poojabhore12/SocialDistancing-Detector
https://github.com/potterhsu/easy-faster-rcnn.pytorch
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/ppokhare/Handling-Imbalanced-Dataset-for-classification-task
https://github.com/pranavjadhav001/detr_keras
https://github.com/pranavmvp/Crowd-Density-Estimator-via-Object-Detection
https://github.com/pransen/faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/preste-ai/camera_ai_whiteboard
https://github.com/preste-nakam/AI_whiteboard
https://github.com/princewang1994/R-FCN.pytorch
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/princewang1994/RFCN_CoupleNet.pytorch
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/pritish-mahajan/Apricitas
https://github.com/protonx-tf-04-projects/ConvNext-2020s
https://github.com/pvdhove/owl-mask-rcnn
https://github.com/pwc-1/Paper-8/tree/main/deformable_detr
Deformable DETR: Deformable Transformers for End-to-End Object Detection
https://github.com/pwc-1/Paper-9/tree/main/1/yolos
YOLOSA: Object detection based on 2D local feature superimposed self-attention
https://github.com/pwc-1/Paper-9/tree/main/5/yolos
YOLOSA: Object detection based on 2D local feature superimposed self-attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.