https://github.com/AlibabaResearch/efficientteacher
공식 코드Efficient Teacher: Semi-Supervised Object Detection for YOLOv5
Papers with Code 아카이브
Semi-Supervised Object Detection
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Semi-supervised object detection uses both labeled data and unlabeled data for training. It not only reduces the annotation burden for training high-performance object detectors but also further improves the object detector by using a large number of unlabeled data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JCZ404/Semi-DETR
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/Kthyeon/ssfod
공식 코드Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
https://github.com/Lans1ng/OSSOD
공식 코드Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
https://github.com/PaddlePaddle/PaddleDetection
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/arsl
공식 코드Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/semi_detr
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/SYangDong/tse-t
공식 코드Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
https://github.com/Wuziyi616/LEOD
공식 코드https://github.com/adamdad/consistentteacher
공식 코드Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
https://github.com/akhilpm/dronessod
공식 코드Density Crop-guided Semi-supervised Object Detection in Aerial Images
https://github.com/amazon-research/omni-detr
공식 코드Omni-DETR: Omni-Supervised Object Detection with Transformers
https://github.com/amirbar/detreg
공식 코드DETReg: Unsupervised Pretraining with Region Priors for Object Detection
https://github.com/azhuantou/hssda
공식 코드Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
https://github.com/cea-list/mt-detr
공식 코드Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
https://github.com/chenbinghui1/dsl
공식 코드https://github.com/czm369/mixpl
공식 코드https://github.com/divadi/detmatch
공식 코드DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
https://github.com/facebookresearch/unbiased-teacher
공식 코드https://github.com/facebookresearch/unbiased-teacher-v2
공식 코드Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
https://github.com/facias914/sood-mcl
공식 코드https://github.com/geoffreychen777/vc
공식 코드Semi-supervised Object Detection via Virtual Category Learning
https://github.com/google-research/ssl_detection
공식 코드A Simple Semi-Supervised Learning Framework for Object Detection
https://github.com/hamperdredes/sood
공식 코드https://github.com/haru-zt/ddpls
공식 코드Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection
https://github.com/hik-lab/ssod
공식 코드https://github.com/hikvision-research/SSOD
공식 코드https://github.com/hunterj-lin/activeteacher
공식 코드https://github.com/implabunipr/unbiased-teacher
공식 코드https://github.com/jh-research/multipleteachers
공식 코드https://github.com/jongmokkim/mix-unmix
공식 코드MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
https://github.com/jshilong/grouprcnn
공식 코드Group R-CNN for Weakly Semi-supervised Object Detection with Points
https://github.com/lexisnexis-risk-open-source/simltd
공식 코드SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection
https://github.com/ligang-cs/PseCo
공식 코드PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
https://github.com/lliuz/mixteacher
공식 코드MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
https://github.com/luluho1208/diffusion-ss3d
공식 코드Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.