https://github.com/MegEngine/Models/tree/master/official/vision/segmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Papers with Code 아카이브
Image Segmentation
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Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MenghaoGuo/AutoDeeplab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Mhaiyang/ICCV2019_MirrorNet
https://github.com/MiRA-lab-dev/SynRec
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
https://github.com/MiguelMonteiro/CRFasRNNLayer
https://github.com/MiguelMonteiro/VNet-Tensorflow
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Mind23-2/MindCode-101
https://github.com/Mind23-2/MindCode-124
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
https://github.com/Mind23-2/MindCode-168
https://github.com/Mind23-2/MindCode-2/tree/main/ViG
ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation
https://github.com/Mind23-2/MindCode-2/tree/main/vnet
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/Mind23-2/MindCode-26
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Mind23-2/MindCode-3
https://github.com/Mind23-2/MindCode-33
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/Mind23-2/MindCode-39
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/Mind23-2/MindCode-44
https://github.com/Mind23-2/MindCode-7
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/MindCode-4/code-2/tree/main/phi
https://github.com/MindCode-4/code-3/tree/main/dinov2
DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
https://github.com/MindCode-4/code-3/tree/main/encoder_decoder
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MindCode-4/code-4/tree/main/sam
https://github.com/MindSpore-paper-code-2/code2/tree/main/nnUNet
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
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/vnet
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepLabV3P
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepLabv3
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-2/code400/tree/main/MobileNet/mobilenetv2
https://github.com/MindSpore-paper-code-3/code1/tree/main/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/MindSpore-paper-code-3/code10/tree/main/fastscnn
https://github.com/MindSpore-paper-code-3/code3/tree/main/fastscnn
https://github.com/MindSpore-paper-code-3/code6/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
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/code7/tree/main/fastscnn
https://github.com/MindSpore-paper-code-3/code8/tree/main/fastscnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.