Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zh272/CGQ-solver
공식 코드Adaptive Learning Rate and Momentum for Training Deep Neural Networks
https://github.com/zh460045050/SNL_ICCV2021
공식 코드https://github.com/zh460045050/vqgan-lc
공식 코드Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%
https://github.com/zhang-xiyue/preimageapproxfornns
공식 코드PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
https://github.com/zhang64-llnl/Mix-n-Match-Calibration
공식 코드Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
https://github.com/zhangbq-research/Prototype_Completion_for_FSL
공식 코드https://github.com/zhangdoudou123/semfew
공식 코드https://github.com/zhanghang1989/PyTorch-Encoding
공식 코드https://github.com/zhanghang1989/ResNeSt
공식 코드https://github.com/zhangluyan9/effhdc
공식 코드https://github.com/zhangxiaozao/BO-Aug
공식 코드Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks
https://github.com/zhangxueting/DCN
공식 코드https://github.com/zhangyifei01/EIR
공식 코드Exploring Instance Relations for Unsupervised Feature Embedding
https://github.com/zhangyuanhan-ai/bamboo
공식 코드Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
https://github.com/zhangzjn/eatformer
공식 코드EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
https://github.com/zhaohengz/llamp
공식 코드Large Language Models are Good Prompt Learners for Low-Shot Image Classification
https://github.com/zhaorui-tan/L-Reg_NeurIPS24
공식 코드https://github.com/zhaowenzhao/mcg_cnn
공식 코드https://github.com/zheminzhang1/multicenter-classifier
공식 코드Generating Multi-Center Classifier via Conditional Gaussian Distribution
https://github.com/zheminzhang96/hand_mammo
공식 코드Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening Mammogram
https://github.com/zhengli97/ctkd
공식 코드https://github.com/zhengliz/natural-adversary
공식 코드https://github.com/zhengtaoyao/lego
공식 코드https://github.com/zhengyushan/kat
공식 코드Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
https://github.com/zhenrongshen/cellgan
공식 코드CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification
https://github.com/zhenyutang2023/data_selection
공식 코드Exploring Data Redundancy in Real-world Image Classification through Data Selection
https://github.com/zhfeing/schemanet-pytorch
공식 코드https://github.com/zhibinliao89/fisher.info.mat.torch
공식 코드Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks
https://github.com/zhicaiwww/diff-mix
공식 코드Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
https://github.com/zhichenghuang/cmae
공식 코드Contrastive Masked Autoencoders are Stronger Vision Learners
https://github.com/zhihou7/batchformer
공식 코드BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
https://github.com/zhiqiangdon/online-augment
공식 코드OnlineAugment: Online Data Augmentation with Less Domain Knowledge
https://github.com/zhiyugege/freqbias
공식 코드Investigating and Explaining the Frequency Bias in Image Classification
https://github.com/zhongping-zhang/movie-clip
공식 코드Movie Genre Classification by Language Augmentation and Shot Sampling
https://github.com/zhongzhuoyao/HCCR-GoogLeNet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.