Papers with Code 아카이브
Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindCode-4/code-7/tree/main/factorized-attention
https://github.com/MindCode-4/code-7/tree/main/group-normalization
https://github.com/MindCode-4/code-7/tree/main/isr
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/MindCode-4/code-8/tree/main/polyloss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindCode-4/code-9/tree/main/ssFPN
ssFPN: Scale Sequence (S^2) Feature Based-Feature Pyramid Network for Object Detection
https://github.com/MindSpore-MS-Code2/code0/tree/main/EGnet
https://github.com/MindSpore-MS-Code2/code0/tree/main/ESRGAN
https://github.com/MindSpore-MS-Code2/code0/tree/main/EfficientDet_d0
https://github.com/MindSpore-MS-Code2/code0/tree/main/detr
https://github.com/MindSpore-MS-Code2/code0/tree/main/efficientnet-b0
https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local
https://github.com/MindSpore-paper-code-2/code2/tree/main/OSVOS
https://github.com/MindSpore-paper-code-2/code2/tree/main/PSPNet
https://github.com/MindSpore-paper-code-2/code2/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/MindSpore-paper-code-2/code2/tree/main/RefineDet
https://github.com/MindSpore-paper-code-2/code2/tree/main/ResNeSt50
https://github.com/MindSpore-paper-code-2/code2/tree/main/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer
https://github.com/MindSpore-paper-code-2/code2/tree/main/relationnet
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
https://github.com/MindSpore-paper-code-2/code2/tree/main/res2net
https://github.com/MindSpore-paper-code-2/code2/tree/main/res2net_deeplabv3
https://github.com/MindSpore-paper-code-2/code2/tree/main/res2net_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-2/code2/tree/main/res2net_yolov3
https://github.com/MindSpore-paper-code-2/code3/tree/main/TokenFusion
https://github.com/MindSpore-paper-code-2/code3/tree/main/UNet3%2B
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_mobilenetV2
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet_34
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/MindSpore-paper-code-2/code3/tree/main/tinydarknet
https://github.com/MindSpore-paper-code-2/code3/tree/main/tracktor
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MindSpore-paper-code-2/code3/tree/main/u2net
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/MindSpore-paper-code-2/code3/tree/main/wave_mlp
https://github.com/MindSpore-paper-code-2/code3/tree/main/yolov3_tiny
https://github.com/MindSpore-paper-code-2/code399/tree/main/Transformer
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.