https://github.com/MindSpore-paper-code-3/code5/tree/main/relationnet
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
Papers with Code 아카이브
2D Classification
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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/res2net_deeplabv3
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
https://github.com/MindSpore-paper-code-3/code5/tree/main/res2net_yolov3
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnetv2_50_frn
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/CBAM
https://github.com/MindSpore-paper-code-3/code6/tree/main/CLIFF
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
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/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
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/code6/tree/main/convnext
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCANet
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCN8s
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/FaceAttribute
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
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/code7/tree/main/ghostnetv2
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
https://github.com/MindSpore-paper-code-3/code8/tree/main/Deepsort
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/FCANet
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code8/tree/main/cspdarknet53
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.