https://github.com/2023-MindSpore-1/ms-code-21
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/CSNL
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DBPN
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/cnn_direction_model
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/Deepsort
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/detr
https://github.com/2023-MindSpore-1/ms-code-212/tree/main/EfficientDet_d0
https://github.com/2023-MindSpore-1/ms-code-212/tree/main/efficientnet-b0
An Efficient and Scalable Deep Learning Approach for Road Damage Detection
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/EGnet
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FCANet
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_dcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_ssod
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/FCOS
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HireMLP
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IPT
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/frustum-pointnet
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/googlenet
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/hardnet
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PSPNet
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineDet
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ResNeSt50
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SE-Net
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SE_ResNeXt50
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.