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 6076–6100 of 10420 papers

TitleStatusHype
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Co-Training 2L Submodels for Visual Recognition—0
A High-Performance Adaptive Quantization Approach for Edge CNN Applications—0
Co-training 2^L Submodels for Visual Recognition—0
Graph Based Convolutional Neural Network—0
On the Robustness of Malware Detectors to Adversarial Samples—0
Graph-Based Classification of Omnidirectional Images—0
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks—0
Grafting Vision Transformers—0
MAAM: A Lightweight Multi-Agent Aggregation Module for Efficient Image Classification Based on the MindSpore Framework—0
MABViT -- Modified Attention Block Enhances Vision Transformers—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Grafit: Learning fine-grained image representations with coarse labels—0
Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building—0
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review—0
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection—0
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data—0
Machine learning for option pricing: an empirical investigation of network architectures—0
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models—0
Minority Reports Defense: Defending Against Adversarial Patches—0
Machine Learning Techniques to Detect and Characterise Whistler Radio Waves—0
Machine Learning with DBOS—0
Machine learning with limited data—0
Machine learning with tree tensor networks, CP rank constraints, and tensor dropout—0
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models—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