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/oliverquintana/UnetSegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/oliverz17/Unet_Keras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/omkar13/MaskTrack

Learning Video Object Segmentation from Static Images

https://github.com/onyame/Git2PROV

Visualization of Contributions to Open-Source Projects

https://github.com/open-cv/deeplab-v1

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

https://github.com/open-cv/deeplab-v2

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

https://github.com/open-edge-platform/geti

Mask R-CNN

https://github.com/open-edge-platform/training_extensions

Mask R-CNN

https://github.com/open-mmlab/mmaction2

Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

https://github.com/open-mmlab/mmclassification

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/open-mmlab/mmdetection

PointRend: Image Segmentation as Rendering

https://github.com/open-mmlab/mmdetection3d

From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

https://github.com/open-mmlab/mmocr

NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition

https://github.com/open-mmlab/mmocr/blob/main/configs/textrecog/svtr/README.md

SVTR: Scene Text Recognition with a Single Visual Model

https://github.com/open-mmlab/mmpose

Microsoft COCO: Common Objects in Context

https://github.com/open-mmlab/mmsegmentation

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

https://github.com/open-mmlab/mmsegmentation/tree/main/configs/vpd

Unleashing Text-to-Image Diffusion Models for Visual Perception

https://github.com/open-mmlab/mmtracking

Deep Feature Flow for Video Recognition

https://github.com/opendilab/DI-engine

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/openmedlab/SAM-Med2D

SAM-Med2D

https://github.com/openrobotlab/ov_parts

Image Segmentation Using Text and Image Prompts

https://github.com/openseg-group/OCNet.pytorch

OCNet: Object Context Network for Scene Parsing

https://github.com/openseg-group/openseg.pytorch

OCNet: Object Context Network for Scene Parsing

https://github.com/orobix/retina-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/osilab-kaist/komet-benchmark-dataset

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/osmr/imgclsmob

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/overshiki/unet-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/oyxhust/CNN-LSTM-CTC-text-recognition

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/p-mc-grath/DMMFODS

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pa56/SegNet_on_Cityscapes

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

https://github.com/pa56/SegNetonCityscapes

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

https://github.com/pacharadanait/MobileNet_V2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/palver7/CFLPytorch

Corners for Layout: End-to-End Layout Recovery from 360 Images

https://github.com/palver7/EquiConvPytorch

Corners for Layout: End-to-End Layout Recovery from 360 Images

https://github.com/panagiotamoraiti/continual-learning-challenge-2024

Learning without Forgetting

https://github.com/parachutel/deeplabv3plus_on_Mapillary_Vistas

Rethinking Atrous Convolution for Semantic Image Segmentation

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