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 5851–5875 of 10420 papers

TitleStatusHype
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping—0
Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction—0
An Efficient and Small Convolutional Neural Network for Pest Recognition -- ExquisiteNet—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Blackbox Trojanising of Deep Learning Models : Using non-intrusive network structure and binary alterations—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing—0
DIME-FM : DIstilling Multimodal and Efficient Foundation Models—0
DIME-FM: DIstilling Multimodal and Efficient Foundation Models—0
An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks—0
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space—0
Dilated Deep Residual Network for Image Denoising—0
Black-box adversarial attacks using Evolution Strategies—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
An Effective Label Noise Model for DNN Text Classification—0
An Effective Gram Matrix Characterizes Generalization in Deep Networks—0
Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks—0
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration—0
Diffusion models applied to skin and oral cancer classification—0
An Effective Fusion Method to Enhance the Robustness of CNN—0
Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification—0
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification—0
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning—0
BiSSL: Enhancing the Alignment Between Self-Supervised Pretraining and Downstream Fine-Tuning via Bilevel Optimization—0
An Effective Anti-Aliasing Approach for Residual Networks—0
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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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified