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 24262450 of 10420 papers

TitleStatusHype
CNN-RNN: A Unified Framework for Multi-label Image ClassificationCode0
CNN Features off-the-shelf: an Astounding Baseline for RecognitionCode0
Interferometric Neural NetworksCode0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
Intelligent Multi-View Test Time AugmentationCode0
A Computing Kernel for Network Binarization on PyTorchCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
C-Net: A Reliable Convolutional Neural Network for Biomedical Image ClassificationCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
Instance Temperature Knowledge DistillationCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Approximate Manifold Defense Against Multiple Adversarial PerturbationsCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Instilling Inductive Biases with SubnetworksCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Initialization Matters for Adversarial Transfer LearningCode0
Input-gradient space particle inference for neural network ensemblesCode0
ClusterFit: Improving Generalization of Visual RepresentationsCode0
Approximate Fisher Information Matrix to Characterise the Training of Deep Neural NetworksCode0
Input Invex Neural NetworkCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Information Competing Process for Learning Diversified RepresentationsCode0
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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