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 8276–8300 of 10420 papers

TitleStatusHype
Improved Mix-up with KL-Entropy for Learning From Noisy Labels—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Benchmarking the Robustness of Semantic Segmentation Models—0
Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware—0
Neural Plasticity NetworksCode0
Atlas: A Dataset and Benchmark for E-commerce Clothing Product CategorizationCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
LIP: Local Importance-based PoolingCode0
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters—0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Space-time error estimates for deep neural network approximations for differential equations—0
Recent Advances in Deep Learning for Object DetectionCode0
Neural Image Compression and ExplanationCode0
Bayesian Inference for Large Scale Image Classification—0
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning—0
On the Variance of the Adaptive Learning Rate and BeyondCode1
Feature selection of neural networks is skewed towards the less abstract cue—0
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data—0
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised LearningCode1
Progressive Transfer LearningCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Deep Self-Learning From Noisy Labels—0
Model Agnostic Defence against Backdoor Attacks in Machine LearningCode1
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning—0
SqueezeNAS: Fast neural architecture search for faster semantic segmentationCode0
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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