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 9951–9975 of 10420 papers

TitleStatusHype
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks—0
An empirical study on the effects of different types of noise in image classification tasksCode0
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding—0
Deep Retinal Image UnderstandingCode0
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes—0
Pruning Filters for Efficient ConvNetsCode2
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition—0
Densely Connected Convolutional NetworksCode1
A 4D Light-Field Dataset and CNN Architectures for Material Recognition—0
Transfer Learning for Endoscopic Image Classification—0
Lets keep it simple, Using simple architectures to outperform deeper and more complex architecturesCode0
Online Feature Selection with Group Structure Analysis—0
Deep Hashing: A Joint Approach for Image Signature Learning—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks—0
Convolutional Oriented BoundariesCode0
Residual Networks of Residual Networks: Multilevel Residual Networks—0
Residual CNDS—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
Play and Learn: Using Video Games to Train Computer Vision Models—0
A study of the effect of JPG compression on adversarial images—0
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations—0
Detecting Visually Relevant Sentences for Fine-Grained Classification—0
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified