https://github.com/OpenGVLab/M3I-Pretraining
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
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/PSCLab-ASU/Learning-in-the-Frequency-Domain
https://github.com/PUTvision/UAVBillboards
https://github.com/PaddlePaddle/PASSL
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/swin
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/PaddlePaddle/PLSC/tree/master/task/ssl/cae
Context Autoencoder for Self-Supervised Representation Learning
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vitdet
Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/PaddlePaddle/PaddleFL
Context Autoencoder for Self-Supervised Representation Learning
https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PaddleOCR
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
https://github.com/PaddlePaddle/PaddleSeg
https://github.com/PaddlePaddle/PaddleYOLO
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/PaddlePaddle/VIMER
Context Autoencoder for Self-Supervised Representation Learning
https://github.com/Pointcept/Pointcept
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
https://github.com/ProhetTeam/QuantMmdetection
https://github.com/Pxtri2156/AdelaiDet_v2
https://github.com/Qdriving/Swin-TransformerV2-in-PP
https://github.com/QiaoranC/tf_ResNeSt_RegNet_model
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
https://github.com/Qinhj07/ATOMCode
https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection
https://github.com/RajArPatra/Super-OCR
https://github.com/Rakeshvd/Instance-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/Rakeshvd/Semantic-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/RangiLyu/mmdetection_test
https://github.com/Res2Net/Res2Net-PoolNet
https://github.com/Res2Net/Res2Net-Pose-Estimation
https://github.com/Res2Net/Res2Net-PretrainedModels
https://github.com/Res2Net/Res2Net-maskrcnn
https://github.com/Res2Net/mmdetection
https://github.com/RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection
https://github.com/RobertHong1992/Resnest
https://github.com/RocketFlash/CAP_augmentation
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
https://github.com/RooKichenn/pytorch-MOAT
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
https://github.com/RuiZhou-cn/U3HS
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
https://github.com/RuiminChen/centernet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.