https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/Shuffle_Transformer
Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
Papers with Code 아카이브
Semantic Segmentation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/VOLO
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BR-IDL/PaddleViT/blob/main/object_detection/DETR
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/CoaT
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/Focal_Transformer
Focal Self-attention for Local-Global Interactions in Vision Transformers
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/HaloNet
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/MobileOne
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/RepMLP
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ResT
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/XCiT
https://github.com/BR-IDL/PaddleViT/tree/develop/self_supervised_learning/dino
https://github.com/BR-IDL/PaddleViT/tree/main/semantic_segmentation
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
https://github.com/BUPT-PRIV/MAE-priv
https://github.com/BUTIYO/vid2vid-test
https://github.com/BYLi-AI/poly-yolo
Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
https://github.com/Baibaifan/An-Improved-PeleeNet-Algorithm-with-Feature-Pyramid-Networks-for-Image-Detection-
https://github.com/Baichenjia/Resnet
https://github.com/BaiqiangGit/15minCode
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BaiqingL/CS523-Final
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Bakikii/stylegan2-pytorch23
https://github.com/Banconxuan/RTM3D
Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training
https://github.com/Bao-Jiarong/ResNet
https://github.com/Barak123748596/CVDL_course_project
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Barchid/RGBD-Seg
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
https://github.com/BardOfCodes/pytorch_deeplab_large_fov
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
https://github.com/Basars/trans-unet
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
https://github.com/BboyHanat/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/BecauseOfGAN/capston
https://github.com/BecauseOfGAN/capstone
https://github.com/BehradToghi/ConvLSTM_VOS
https://github.com/BehradToghi/ECCV_Youtube_VOS
https://github.com/BenediktAlkin/vtab1k-pytorch
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
https://github.com/BenjiKCF/Superesolution-and-watermark-removal
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.