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 17011725 of 10420 papers

TitleStatusHype
Efficient On-device Training via Gradient FilteringCode1
Freestyle Layout-to-Image SynthesisCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
Efficient ResNets: Residual Network DesignCode1
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and LanguageCode1
Efficient Self-supervised Vision Pretraining with Local Masked ReconstructionCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image ClassificationCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic SparsityCode1
From ImageNet to Image Classification: Contextualizing Progress on BenchmarksCode1
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network CalibrationCode1
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelCode1
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy LabelsCode1
Eigenspectrum Analysis of Neural Networks without Aspect Ratio BiasCode1
Age Estimation Using Expectation of Label Distribution LearningCode1
ELSA: Enhanced Local Self-Attention for Vision TransformerCode1
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamicsCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature FusionCode1
PRIME: A few primitives can boost robustness to common corruptionsCode1
Emerging Properties in Self-Supervised Vision TransformersCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features SelectionCode1
CLCC: Contrastive Learning for Color ConstancyCode1
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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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified