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 1015110175 of 10420 papers

TitleStatusHype
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks0
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and TreeCode0
Mapping Generative Models onto a Network of Digital Spiking Neurons0
Learning Wake-Sleep Recurrent Attention Models0
Tensorizing Neural NetworksCode0
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data0
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic StudyCode0
Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks0
DeepSat - A Learning framework for Satellite ImageryCode0
Deep Attributes from Context-Aware Regional Neural Codes0
Image Classification with Rejection using Contextual Information0
Computational Integration of Human Vision and Natural Language through Bitext Alignment0
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification0
Fisher Vectors Meet Neural Networks: A Hybrid Classification Architecture0
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data0
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy0
INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks0
Local Color Contrastive Descriptor for Image Classification0
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units0
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification0
Neural NILM: Deep Neural Networks Applied to Energy DisaggregationCode0
Towards Effective Codebookless Model for Image Classification0
Feature Representation in Convolutional Neural Networks0
DCTNet : A Simple Learning-free Approach for Face Recognition0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified