Papers with Code 아카이브
Segmentation
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/HyeonwooNoh/caffe
https://github.com/HzFu/DENet_GlaucomaScreen
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
https://github.com/HzFu/MNet_DeepCDR
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
https://github.com/IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization
3D MRI brain tumor segmentation using autoencoder regularization
https://github.com/IAmSuyogJadhav/Brainy
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IBM/MAX-Object-Detector
https://github.com/IDEA-opensource/DAB-DETR
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
https://github.com/IDEA-opensource/DN-DETR
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
https://github.com/IDEACVR/DINO
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
https://github.com/IE-group/conference_report
3D MRI brain tumor segmentation using autoencoder regularization
https://github.com/IMPLabUniPr/mmdetection
A novel Region of Interest Extraction Layer for Instance Segmentation
https://github.com/IMPLabUniPr/mmdetection-groie
A novel Region of Interest Extraction Layer for Instance Segmentation
https://github.com/IMvision12/keras-vision-models
https://github.com/IRVLab/SUIM
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IRVLab/SUIM-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Iamanorange/DSSLIC
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
https://github.com/Iammuratc/grainDetection
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/Ian-Liao/SegCaps
https://github.com/IgorZaton/Mini-Projects
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IllIIIllll/where-is-wally
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
https://github.com/IlliaOvcharenko/lung-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ImagingLab/Colorizing-with-GANs
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/InsaneLife/ChineseNLPCorpus
https://github.com/IntelLabs/AVUC
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
https://github.com/IntelligenceDatum/ICCV2019_Model_Compression
https://github.com/IsaacPatole/TGS-Salt-Identification-Challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IuliiaZaitova/code_slingers
https://github.com/IzPerfect/CT_Image_Segmentation
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
https://github.com/J-jjj7230/u-net_model
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JHLee0513/Salt_detection_challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JLrumberger/SpatialEmbeddingLoss
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
https://github.com/JWuzyk/CudaVisionProject
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Jackey9797/FCN
https://github.com/JaidedAI/EasyOCR
Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.