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

Showing 95769600 of 10420 papers

TitleStatusHype
An Intriguing Failing of Convolutional Neural Networks and the CoordConv SolutionCode0
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural NetworkCode0
Recurrent computations for visual pattern completionCode0
ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic RegressionCode0
Targeted Deep Learning System Boundary TestingCode0
Large-Scale Evaluation of Open-Set Image Classification TechniquesCode0
Large-Scale Evolution of Image ClassifiersCode0
Deep learning in a bilateral brain with hemispheric specializationCode0
Saliency Maps Give a False Sense of Explanability to Image Classifiers: An Empirical Evaluation across Methods and MetricsCode0
Practical Block-wise Neural Network Architecture GenerationCode0
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnosticsCode0
Neural Architecture Search with Reinforcement LearningCode0
Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin MemoryCode0
Recurrent Highway Networks with Grouped Auxiliary MemoryCode0
Bivariate Beta-LSTMCode0
LarvSeg: Exploring Image Classification Data For Large Vocabulary Semantic Segmentation via Category-wise Attentive ClassifierCode0
Deep Learning for Identifying Metastatic Breast CancerCode0
LatentAugment: Dynamically Optimized Latent Probabilities of Data AugmentationCode0
Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAMCode0
Activations and Gradients Compression for Model-Parallel TrainingCode0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networksCode0
Latent State Models of Training DynamicsCode0
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision TransformersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified