https://github.com/leohsuofnthu/Pytorch-IterativeFCN
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Papers with Code 아카이브
Instance Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 36
- 보존된 평가 기록
- 338
Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/leohsuofnthu/Tensorflow-YOLACT
https://github.com/leonardhan1979/fasterRCNN
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/botnet
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/coat
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/cotnet
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/davit
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientvit
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/gcvit
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/gpvit
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/halonet
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hiera
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/moganet
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/nat
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/resnest
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/resnet_family
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/swin_transformer_v2
https://github.com/lessw2020/auto-adaptive-ai
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/lexisnexis-risk-open-source/ledetection
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/lhwcv/mmdet
https://github.com/liamlian0727/watermask
https://github.com/libffcv/ffcv-imagenet
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
https://github.com/lichengunc/mask-faster-rcnn
https://github.com/lifuguan/SOLOv2-mmdet
https://github.com/lifunudt/blender_based_render
https://github.com/ligaoqi2/Pose2Seg-single-person-video-demo
https://github.com/lightly-ai/lightly
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
https://github.com/lincaiming/py-faster-rcnn-update
https://github.com/lincaiming/py-faster-rcnn-windows
https://github.com/lippman1125/mmdetection_lqy
https://github.com/liuruiyang98/Jittor-MLP
https://github.com/liuxingwt/CLS
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/lixiaolei1982/DenseNet-base-Mask-RCNN-for-Human-Pose-Estimation
https://github.com/liyy201912/PaS
https://github.com/lizhe960118/CenterNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.