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 5826–5850 of 10420 papers

TitleStatusHype
Detecting Novelties with Empty Classes—0
An Analysis of Pre-Training on Object Detection—0
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing—0
Detecting Overfitting via Adversarial Examples—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Learning to Learn Image Classifiers with Visual Analogy—0
Attending Category Disentangled Global Context for Image Classification—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Making Vision Transformers Truly Shift-Equivariant—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks—0
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification—0
Detecting Visually Relevant Sentences for Fine-Grained Classification—0
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability—0
Coverage Testing of Deep Learning Models using Dataset Characterization—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
flexgrid2vec: Learning Efficient Visual Representations Vectors—0
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device—0
Learning to Sample: an Active Learning Framework—0
Learning to Schedule Learning rate with Graph Neural Networks—0
Learning to see across Domains and Modalities—0
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Covariance-corrected Whitening Alleviates Network Degeneration on Imbalanced Classification—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
10RevCol-HTop 1 Accuracy90—Unverified