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Semi Supervised Object Detection On Coco 100

Metrics

mAP

Results

Performance results of various models on this benchmark

Model Name
mAP
Paper TitleRepository
Revisiting Class Imbalance44.0Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection-
Semi-DETR50.5Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
MixPL55.2Mixed Pseudo Labels for Semi-Supervised Object Detection
MUM42.11MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
STAC39.2A Simple Semi-Supervised Learning Framework for Object Detection
PseCo46.1PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Consistent-Teacher48.20Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
UNBIASED TEACHER41.3Unbiased Teacher for Semi-Supervised Object Detection
RPL43.3Rethinking Pseudo Labels for Semi-Supervised Object Detection-
Instant-Teaching40.2Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
Adaptive Class-Rebalancing42.79Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training-
Soft Teacher44.9End-to-End Semi-Supervised Object Detection with Soft Teacher
Dense Teacher46.2Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
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