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 9851–9875 of 10420 papers

TitleStatusHype
PatternNet: Visual Pattern Mining with Deep Neural Network—0
From visual words to a visual grammar: using language modelling for image classification—0
Convolutional Low-Resolution Fine-Grained Classification—0
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge—0
Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge—0
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation—0
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks—0
Evaluating Deep Convolutional Neural Networks for Material Classification—0
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network EnsembleCode0
Deep Learning applied to NLPCode0
Learning to Remember Rare EventsCode0
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute DetectionCode0
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-taggingCode0
Large-Scale Evolution of Image ClassifiersCode0
Adversarial Examples for Semantic Image Segmentation—0
Deep Collaborative Learning for Visual Recognition—0
Learning What Data to Learn—0
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks—0
ShaResNet: reducing residual network parameter number by sharing weightsCode0
Learning Deep NBNN Representations for Robust Place Categorization—0
Mimicking Ensemble Learning with Deep Branched Networks—0
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification—0
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image ClassificationCode0
A Survey on Deep Learning in Medical Image Analysis—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