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 20512075 of 10420 papers

TitleStatusHype
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
DIFFender: Diffusion-Based Adversarial Defense against Patch AttacksCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
A Single Graph Convolution Is All You Need: Efficient Grayscale Image ClassificationCode1
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural NetworkCode1
Consistency-based Active Learning for Object DetectionCode1
Differentiable Model Compression via Pseudo Quantization NoiseCode1
Direct Differentiable Augmentation SearchCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
DetCo: Unsupervised Contrastive Learning for Object DetectionCode1
Detecting AutoAttack Perturbations in the Frequency DomainCode1
A Simple Semi-Supervised Learning Framework for Object DetectionCode1
Conformer: Local Features Coupling Global Representations for Visual RecognitionCode1
A Simple Interpretable Transformer for Fine-Grained Image Classification and AnalysisCode1
Depth Uncertainty in Neural NetworksCode1
Designing Network Design SpacesCode1
DenoiseRep: Denoising Model for Representation LearningCode1
Dense Contrastive Learning for Self-Supervised Visual Pre-TrainingCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
Densely Connected Convolutional NetworksCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data AugmentationCode1
Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small DatasetsCode1
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural 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