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 726750 of 10419 papers

TitleStatusHype
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial VehiclesCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
AdaScale SGD: A User-Friendly Algorithm for Distributed TrainingCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Deep Network Ensemble Learning applied to Image Classification using CNN TreesCode1
Deep Networks with Stochastic DepthCode1
A Data Set and a Convolutional Model for Iconography Classification in PaintingsCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
AdaViT: Adaptive Tokens for Efficient Vision TransformerCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class ForgettingCode1
DeepViT: Towards Deeper Vision TransformerCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed DatasetsCode1
Delving into Out-of-Distribution Detection with Medical Vision-Language ModelsCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
DenoiseRep: Denoising Model for Representation LearningCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz NetworksCode1
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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