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/polospeter/TensorFlow-Advanced-Techniques-Specialization

Mask R-CNN

https://github.com/prajjwal1/autonomous-object-detection

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

https://github.com/pranjalrai-iitd/Fetal-head-segmentation-and-circumference-measurement-from-ultrasound-images

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pranoy-panda/multi_class_segmentation_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/prathameshza/TrOCR_FineTuning

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

https://github.com/preddy5/segnet

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

https://github.com/preritj/segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pricebenjamin/unet-estimator

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/princeton-computational-imaging/SeeingThroughFog

Gated2Depth: Real-time Dense Lidar from Gated Images

https://github.com/pripri99/Floorplan-Generation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/prithivirajdamodaran/styleformer

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

https://github.com/purushothamgowthu/deep-photo-styletransfer

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

https://github.com/pvdhove/owl-mask-rcnn

Mask R-CNN

https://github.com/pvjosue/LFMNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pwc-1/Paper-10/tree/main/sam

Segment Anything

https://github.com/pwc-1/Paper-10/tree/main/segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/pwc-1/Paper-10/tree/main/seggpt

SegGPT: Segmenting Everything In Context

https://github.com/pwc-1/Paper-10/tree/main/trocr

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

https://github.com/pwc-1/Paper-5/tree/main/mask2former

Mask2Former for Video Instance Segmentation

https://github.com/pwc-1/Paper-5/tree/main/mctct

MCTS with Refinement for Proposals Selection Games in Scene Understanding

https://github.com/pwc-1/Paper-5/tree/main/mobilenet_v1

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/pwc-1/Paper-5/tree/main/mobilenet_v2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/pwc-1/Paper-9/tree/main/1/trocr

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

https://github.com/pwc-1/Paper-9/tree/main/2/mctct

MCTS with Refinement for Proposals Selection Games in Scene Understanding

https://github.com/pwc-1/Paper-9/tree/main/2/mobilenet_v2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/pwc-1/Paper-9/tree/main/5/upernet

Unified Perceptual Parsing for Scene Understanding

https://github.com/pwc-1/Paper-9/tree/main/groupvit

GroupViT: Semantic Segmentation Emerges from Text Supervision

https://github.com/pwochner/cmr_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pydsgz/DeepVOG

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pytorch/vision

Fully Convolutional Networks for Semantic Segmentation

https://github.com/qfzhu/st

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/qianpeisheng/Unet_semicon

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/qianyu-dlut/DiffDIS

High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity

https://github.com/qilei123/pyfasterrcnn

Mask R-CNN

https://github.com/qjadud1994/CRNN-Keras

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

https://github.com/qq330488563/TEST

Mask R-CNN

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