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/mstfakts/building-detection-maskrcnn

Mask R-CNN

https://github.com/mtancak/PyTorch-UNet-Brain-Cancer-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mtancak1/PyTorch-UNet-BraTS20

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mtancak1/PyTorch-UNet-Brain-Cancer-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/muellerdo/covid19.MIScnn

Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net

https://github.com/multimodallearning/pytorch-mask-rcnn

Mask R-CNN

https://github.com/muramasa8191/DeepLearning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/murufeng/awesome_lightweight_networks

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

https://github.com/muyistarsky/MaskRCNN

Mask R-CNN

https://github.com/mystorm16/bsh-det3d

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

https://github.com/nachifur/DMTN

Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN

https://github.com/nalbert9/Image-Captioning

Microsoft COCO: Common Objects in Context

https://github.com/namanphy/style-transfer-through-back-translate

Style Transfer Through Back-Translation

https://github.com/napolar/simple_unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/natgable/cs-229-project

Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks

https://github.com/naveen-kumar-123/Handwritten-text-recognition---CNN-and-LSTM

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

https://github.com/naver-ai/BlendNeRF

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/naver-ai/coco-annotation-tool

Microsoft COCO: Common Objects in Context

https://github.com/navganti/SIVO

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

https://github.com/navganti/SegNet

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

https://github.com/navidhasanzadeh/UNet-Semantic-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/nbouteldja/kidneystaintranslation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/nchang430/BOLD5000-Scripts

BOLD5000: A public fMRI dataset of 5000 images

https://github.com/neerakara/TTA_prostate

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

https://github.com/nellish123/contrib

D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation

https://github.com/neptune-ai/open-solution-mapping-challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/nero1342/VATEX

Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding

https://github.com/neshitov/UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/neuleaf/MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/neuroailab/LocalAggregation-Pytorch

Local Aggregation for Unsupervised Learning of Visual Embeddings

https://github.com/neuropoly/axondeepseg

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/neuropoly/multiclass-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/nexusapoorvacus/DeepVariationStructuredRL

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

https://github.com/ngailapdi/LWF

Learning without Forgetting

https://github.com/nhatuan84/GFF-Gated-Fully-Fusion-for-Semantic-Segmentation

GFF: Gated Fully Fusion for Semantic Segmentation

https://github.com/nianticlabs/simplerecon

SimpleRecon: 3D Reconstruction Without 3D Convolutions

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