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/harsh2011/Yolov3-Detector
https://github.com/hellohaozheng/maskrcnn-mindspore
https://github.com/hellotrik/torch_yolov3
https://github.com/henrytsui000/YOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/hexhowells/Neural-Network-Implementations/tree/main/models/ParNet
https://github.com/hhk7734/tensorflow-yolov4
https://github.com/hhuaibo/darknet
https://github.com/hirohiro23/Darknet
https://github.com/hisiter97/darknet
https://github.com/hj963434688/Faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/hmichaeli/alias_free_convnets
https://github.com/hnmspirit/malaria_centernet
https://github.com/hoanguyen81190/Synopticon-CenterNet
https://github.com/holdfire/CLS
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/holdfire/FAS
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/hollyprince/ObjectsDetection-CornerNet-Lite
https://github.com/holytrick/AlexeyAB_darknet
https://github.com/horrible-dong/teamdetr
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/houssemjebari/Fruit-Detection
https://github.com/hpc203/YOLObile-opencv-dnn
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
https://github.com/hsfzxjy/swavx
https://github.com/huan123/py-fatser-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/huggingface/transformers
https://github.com/hunglc007/tensorflow-yolov4-tflite
https://github.com/hunter10bt/DeepLearningFinalPresentation
https://github.com/hustvl/sparseinst
https://github.com/husty530/Yamashita-darknet
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hwillard98/htmlify-yolo-training
https://github.com/hyang0129/foodclassapp
https://github.com/iamsingh/darknet
https://github.com/ibaiGorordo/ONNX-YOLOv10-Object-Detection
https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.