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 6101–6125 of 10420 papers

TitleStatusHype
Learning to Teach—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning—0
mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
Learning to See Physical Properties with Active Sensing Motor Policies—0
Maintaining Performance with Less Data—0
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery—0
Make A Long Image Short: Adaptive Token Length for Vision Transformers—0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning—0
Learning to see across Domains and Modalities—0
Learning to Schedule Learning rate with Graph Neural Networks—0
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning—0
Learning to Sample: an Active Learning Framework—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 Rank for Active Learning: A Listwise Approach—0
Learning to predict visual brain activity by predicting future sensory states—0
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.—0
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning—0
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
Malaria detection from RBC images using shallow Convolutional Neural Networks—0
Learning to Name Classes for Vision and Language Models—0
Detecting Visually Relevant Sentences for Fine-Grained 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
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