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 42264250 of 10420 papers

TitleStatusHype
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural NetworksCode0
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
Contrastive Learning for OOD in Object detectionCode0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
Human-imperceptible, Machine-recognizable ImagesCode0
Contrastive-center loss for deep neural networksCode0
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image ClassificationCode0
A Genetic Programming Approach to Designing Convolutional Neural Network ArchitecturesCode0
How to Use Dropout Correctly on Residual Networks with Batch NormalizationCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
How transfer learning is used in generative models for image classification: improved accuracyCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Hyperspectral Image Classification in the Presence of Noisy LabelsCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Continuous Meta-Learning without TasksCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
Continual Learning with Strong Experience ReplayCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
Histogram Layers for Neural Engineered FeaturesCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
How Do Training Methods Influence the Utilization of Vision Models?Code0
Show:102550
← PrevPage 170 of 417Next →

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