Papers with Code 아카이브
Dense Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Megvii-BaseDetection/AutoAssign
공식 코드AutoAssign: Differentiable Label Assignment for Dense Object Detection
https://github.com/Megvii-BaseDetection/BorderDet
공식 코드https://github.com/PaddlePaddle/PaddleDetection
공식 코드https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/arsl
공식 코드Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
https://github.com/ParallelDots/generic-sku-detection-benchmark
공식 코드Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection
https://github.com/XavierYoungY/SALT-Net
공식 코드Rethinking the Misalignment Problem in Dense Object Detection
https://github.com/alivaramesh/MixtureDenseRegression
공식 코드Mixture Dense Regression for Object Detection and Human Pose Estimation
https://github.com/chenhongyiyang/pgd
공식 코드https://github.com/eg4000/SKU110K_CVPR19
공식 코드https://github.com/facebookresearch/detectron
공식 코드https://github.com/implus/GFocal
공식 코드Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
https://github.com/implus/GFocalV2
공식 코드https://github.com/jbwang1997/crosskd
공식 코드CrossKD: Cross-Head Knowledge Distillation for Object Detection
https://github.com/sigma10010/nuclei_cells_det
공식 코드SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images
https://github.com/tinytigerpan/bckd
공식 코드Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
https://github.com/trumpetertimes/sparse-generation
공식 코드https://github.com/xiuqhou/Salience-DETR
공식 코드Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
https://github.com/xudangliatiger/ape-loss
공식 코드Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet
https://github.com/2023-MindSpore-1/ms-code-44
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaNet
https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv5
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet
https://github.com/497626895/darknet
https://github.com/AdeelH/pytorch-multi-class-focal-loss
https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection
Robust and Efficient Post-Processing for Video Object Detection (REPP)
https://github.com/AlexeyAB/darknet
https://github.com/AtlasCoCo/Darknet
https://github.com/Burf/tfdetection
https://github.com/CV51GO/GFLv2_Megengine
https://github.com/CahideSara/deneme
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.