Papers with Code 아카이브

2D Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

17

평가표

11

보존된 평가 기록

42

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AlphaJia/keras_unet_plus_plus

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/Amisha-here/Image-Classifier

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AmrElsersy/PointPainting

Image Segmentation Using Deep Learning: A Survey

https://github.com/AndPuQing/ContextPrior_Paddle

Context Prior for Scene Segmentation

https://github.com/AndreiMoraru123/Hydra

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AndresPMD/Pytorch-yolo-phoc

Single Shot Scene Text Retrieval

https://github.com/Andrew-Qibin/ssdlite-pytorch

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Andrew-booler/W-Net

W-Net: A Deep Model for Fully Unsupervised Image Segmentation

https://github.com/Aniket7/Transfer-Learning

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

https://github.com/Animadversio/FloodFillNetwork-Notes

Flood-Filling Networks

https://github.com/Anirudh0707/Roads-and-Building-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AnirudhAchal/Human-Parsing

Pyramid Scene Parsing Network

https://github.com/ArantxaCasanova/fc-drn

On the iterative refinement of densely connected representation levels for semantic segmentation

https://github.com/ArashJavan/FOTS

FOTS: Fast Oriented Text Spotting with a Unified Network

https://github.com/Arcady1/Doodle-Recognition-Web

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/ArkaJU/SegNet---Chromosome

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/ArkaJU/U-Net-Satellite

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Arnukk/CASPIAN

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/Arthur-Shi/py-faster-rcnn

Mask R-CNN

https://github.com/Artsplendr/Medical-Images-Liver-Segmentation-NIfTI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Artsplendr/Medical-Images-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AryaKoureshi/Brain-tumor-detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Aryavir07/Detecting-Brain-Tumor-Using-Deep-Learning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AsafBarZvi/Liver_project

Conditional Random Fields as Recurrent Neural Networks

https://github.com/AshishSingh2261/Pedestrian_Instance_Segmentation

Mask R-CNN

https://github.com/Asthestarsfalll/IBNNet-MegEngine

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/Asthestarsfalll/PSPNet-MegEngine

Pyramid Scene Parsing Network

https://github.com/AtrayeeNag/Image-Segmentation-deeplabv3

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/AutomatedAI/deeplab_inference

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/AutomatedAI/deeplab_segmentation_example

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/AviTewari/Lake-Ice-Monitoring

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Axeldnahcram/biconvex_relaxation

A Fast Semidefinite Approach to Solving Binary Quadratic Problems

https://github.com/AyaLotfy/PReMVOS-trial

PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation

https://github.com/AyaLotfy/premvos-tmp

PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation

https://github.com/Aykhan-sh/Non-local-U-Net-Pytorch

Non-local U-Net for Biomedical Image Segmentation

https://github.com/AzogDefiler/Sandbox

Fully Convolutional Networks for Semantic Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.