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

Showing 94769500 of 10420 papers

TitleStatusHype
PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image ClassificationCode0
An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine CompositesCode0
PolyNet: A Pursuit of Structural Diversity in Very Deep NetworksCode0
Deep Residual Learning in the JPEG Transform DomainCode0
Born Again Neural NetworksCode0
Augmenting Deep Classifiers with Polynomial Neural NetworksCode0
KGTN-ens: Few-Shot Image Classification with Knowledge Graph EnsemblesCode0
Deep regularization and direct training of the inner layers of Neural Networks with Kernel FlowsCode0
Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie StimuliCode0
NAS evaluation is frustratingly hardCode0
Deep Pyramidal Residual NetworksCode0
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese NetworksCode0
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature ForgettingCode0
ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image DatasetCode0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
Deep Online Probability Aggregation ClusteringCode0
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Knowledge Distillation by On-the-Fly Native EnsembleCode0
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial NetworksCode0
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost TuningCode0
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural NetworksCode0
Knowledge Distillation from Single to Multi Labels: an Empirical StudyCode0
RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image ClassificationCode0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
A Gradient Boosting Approach for Training Convolutional and Deep Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified