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/qrzyang/pseudo-stereo

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/qsyao/cuda_spatial_deform

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/qubvel/segmentation_models

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/qubvel/segmentation_models.pytorch

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

https://github.com/quocdat32461997/Mask_RCNN

Mask R-CNN

https://github.com/rachthree/fast_scnn

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/raghavian/mltn

Lung Segmentation from Chest X-rays using Variational Data Imputation

https://github.com/rahul13ramesh/cloud_understanding

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/raisinglc/object_detection_SSD

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/rajatashhpa/1

Microsoft COCO: Common Objects in Context

https://github.com/rajatkoner08/Graphhopper

Scenes and Surroundings: Scene Graph Generation using Relation Transformer

https://github.com/rajmuchhala/Raj-Muchhala---Sr.-ML-Engineer--ML-Assignmen

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

https://github.com/raspstephan/sugar-flower-fish-or-gravel

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/raun1/Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/raun1/ISBI-2020-LITS_Hybrid_Comp_Net

CompNet: Complementary Segmentation Network for Brain MRI Extraction

https://github.com/raun1/LITS_Hybrid_Comp_Net

CompNet: Complementary Segmentation Network for Brain MRI Extraction

https://github.com/raun1/MICCAI2018---Complementary_Segmentation_Network-Raw-Code

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/raun1/MICCAI2018---Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/raunak-sood2003/Automated-Lung-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/raunak222/Image-Captioning

Microsoft COCO: Common Objects in Context

https://github.com/ravenwritingdesk/py-faster-rcnn-master

Mask R-CNN

https://github.com/ray-project/ray/tree/master/rllib

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

https://github.com/raymon-tian/hourglass-facekeypoints-detection

Mask R-CNN

https://github.com/raymondwang987/fmnet

NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation

https://github.com/razeineldin/DeepSeg

Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI

https://github.com/rdadlaney/Audio-Denoiser-CNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/rdevon/BGAN

Boundary-Seeking Generative Adversarial Networks

https://github.com/realmichaelye/NLP-State-of-the-art-Style-Transfer

Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

https://github.com/reenal/melanoma-skin-cancer-image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/rekon/smoke-semantic-segmentation

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

https://github.com/rexxxx1234/SAUNet-demo

SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

https://github.com/reyvaz/pneumothorax_detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/reyvaz/steel-defect-segmentation

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

https://github.com/rezazad68/smunet

Contextual Attention Network: Transformer Meets U-Net

https://github.com/rh01/fast-rnn

Mask R-CNN

https://github.com/rh01/faster-rcnn

Mask R-CNN

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