https://github.com/MindSpore-paper-code-3/code5/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
https://github.com/MindSpore-paper-code-3/code5/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/MindSpore-paper-code-3/code6/tree/main/CascadeRCNN
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
https://github.com/MindSpore-paper-code-3/code6/tree/main/adelaide_ea
https://github.com/MindSpore-paper-code-3/code6/tree/main/centernet
https://github.com/MindSpore-paper-code-3/code6/tree/main/centernet_det
https://github.com/MindSpore-paper-code-3/code6/tree/main/centernet_resnet50_v1
https://github.com/MindSpore-paper-code-3/code6/tree/main/cnnctc
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCOS
NAS-FCOS: Fast Neural Architecture Search for Object Detection
https://github.com/MindSpore-paper-code-3/code7/tree/main/fairmot
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/MindSpore-paper-code-3/code7/tree/main/faster_rcnn_dcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-3/code7/tree/main/frustum-pointnet
https://github.com/MindSpore-paper-code-3/code8/tree/main/EGnet
https://github.com/MindSpore-paper-code-3/code8/tree/main/EfficientDet_d0
https://github.com/MindSpore-paper-code-3/code8/tree/main/cspdarknet53
https://github.com/MindSpore-paper-code-3/code8/tree/main/fairmot
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/MindSpore-paper-code-3/code8/tree/main/faster_rcnn_dcn
https://github.com/MindSpore-paper-code-3/code9/tree/main/DYR
DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors
https://github.com/MindSpore-paper-code-3/code9/tree/main/Efficientnet
https://github.com/MindSpore-paper-code-3/code9/tree/main/FasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN
https://github.com/MindSpore-paper-code-3/code9/tree/main/OCRNet
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/SSD
https://github.com/MindSpore-scientific-2/code-12/tree/main/group-normalization
https://github.com/MindSpore-scientific-2/code-14/tree/main/mamba
Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
https://github.com/MindSpore-scientific-2/code-2/tree/main/SING
https://github.com/MindSpore-scientific-2/code-3/tree/main/Tom
TOM-Net: Learning Transparent Object Matting from a Single Image
https://github.com/MindSpore-scientific-2/code-4/tree/main/Deformable_Patch_Representation
https://github.com/MindSpore-scientific-2/code-4/tree/main/fisheradversarial-mindspore
Robust and Accurate Object Detection via Adversarial Learning
https://github.com/MindSpore-scientific-2/code-5/tree/main/Deformable_Patch_Representation
https://github.com/MindSpore-scientific-2/code-5/tree/main/fisheradversarial-mindspore
Robust and Accurate Object Detection via Adversarial Learning
https://github.com/MindSpore-scientific-2/code-8/tree/main/SING
https://github.com/MindSpore-scientific/code-10/tree/main/SING
https://github.com/MindSpore-scientific/code-11/tree/main/SING
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.