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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/prakharg24/yoloret
공식 코드YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs
https://github.com/previtus/AttentionPipeline
공식 코드Fast and accurate object detection in high resolution 4K and 8K video using GPUs
https://github.com/princeton-vl/CornerNet-Lite
공식 코드https://github.com/qiujiaming315/edgeml-object-detection
공식 코드https://github.com/razaimam45/fine-tune-distillation
공식 코드Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance
https://github.com/rbgirshick/py-faster-rcnn
공식 코드Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/rubbbbbbbbby/swinoir
공식 코드https://github.com/ruinmessi/RFBNet
공식 코드Receptive Field Block Net for Accurate and Fast Object Detection
https://github.com/sacmehta/EdgeNets
공식 코드ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://github.com/sanghoon/pva-faster-rcnn
공식 코드PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
https://github.com/shihuahuang95/deim
공식 코드https://github.com/shinya7y/UniverseNet
공식 코드https://github.com/shubha07m/On-device-computer-vision-experiments-with-IoT
공식 코드MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO
https://github.com/smcch/brain_tumor_detection_ultrasound
공식 코드https://github.com/tchittesh/fovea
공식 코드FOVEA: Foveated Image Magnification for Autonomous Navigation
https://github.com/tensorflow/models/tree/master/official/vision/beta/modeling/backbones
공식 코드SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/tensorflow/models/tree/master/research/lstm_object_detection
공식 코드Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
https://github.com/tensorflow/tpu
공식 코드MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/tinyvision/damo-yolo
공식 코드https://github.com/trbg/vagus-nerve-u-net
공식 코드https://github.com/viswas1/TinyML
공식 코드Real Time Object Detection System with YOLO and CNN Models: A Review
https://github.com/wongkinyiu/yolov7
공식 코드YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/xingyizhou/CenterNet
공식 코드https://github.com/yancie-yjr/StreamYOLO
공식 코드https://github.com/yang-0201/MAF-YOLO
공식 코드https://github.com/yoojy31/mdod
공식 코드Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
https://github.com/yoshitomo-matsubara/hnd-ghnd-object-detectors
공식 코드Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
https://github.com/youngwanLEE/CenterMask
공식 코드https://github.com/zhaoweicai/mscnn
공식 코드A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
https://dagshub.com/Bharat-mtr/yolov1-tf2
https://dagshub.com/arnavr.neo/YOLOX
https://github.com/0jason000/convnext
https://github.com/1512159/tf-faster-rcnn-medico
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/1991yuyang/YOLOV1-PYTORCH
https://github.com/2023-MindSpore-1/ms-code-117
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.