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 12261250 of 10420 papers

TitleStatusHype
Fast Hierarchical Games for Image ExplanationsCode1
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image ClassificationCode1
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
A Universal Representation Transformer Layer for Few-Shot Image ClassificationCode1
BinaryViT: Pushing Binary Vision Transformers Towards Convolutional ModelsCode1
A Bregman Learning Framework for Sparse Neural NetworksCode1
DynamicViT: Efficient Vision Transformers with Dynamic Token SparsificationCode1
FCCNs: Fully Complex-valued Convolutional Networks using Complex-valued Color Model and Loss FunctionCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
AutoDC: Automated data-centric processingCode1
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)Code1
AutoDiCE: Fully Automated Distributed CNN Inference at the EdgeCode1
Class-Aware Contrastive Semi-Supervised LearningCode1
Class-Balanced Loss Based on Effective Number of SamplesCode1
Class Adaptive Network CalibrationCode1
Revisiting the Importance of Amplifying Bias for DebiasingCode1
Class-Aware Patch Embedding Adaptation for Few-Shot Image ClassificationCode1
Class-Balanced Distillation for Long-Tailed Visual RecognitionCode1
Auto Learning AttentionCode1
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the FlyCode1
Aggregated Residual Transformations for Deep Neural NetworksCode1
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
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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