SOTAVerified

Image Classification

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.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2101–2125 of 10420 papers

TitleStatusHype
Distilling Object Detectors via Decoupled FeaturesCode1
Consistency-based Active Learning for Object DetectionCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
DiT: Self-supervised Pre-training for Document Image TransformerCode1
Diverse Branch Block: Building a Convolution as an Inception-like UnitCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
Continual Learning Using a Kernel-Based Method Over Foundation ModelsCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
DLME: Deep Local-flatness Manifold EmbeddingCode1
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical ImagesCode1
A Fully Tensorized Recurrent Neural NetworkCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Domain Adaptation for Multi-label Image Classification: a Discriminator-free ApproachCode1
Domain-Adversarial Training of Neural NetworksCode1
Do text-free diffusion models learn discriminative visual representations?Code1
Do Vision and Language Encoders Represent the World Similarly?Code1
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?Code1
Container: Context Aggregation NetworkCode1
A fuzzy distance-based ensemble of deep models for cervical cancer detectionCode1
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical CytologyCode1
Dual-Branch Subpixel-Guided Network for Hyperspectral Image ClassificationCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
Show:102550
← PrevPage 85 of 417Next →
All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified