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 12761300 of 10420 papers

TitleStatusHype
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Automating Continual LearningCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
AutoMix: Unveiling the Power of Mixup for Stronger ClassifiersCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
BinaryViT: Pushing Binary Vision Transformers Towards Convolutional ModelsCode1
Efficient-CapsNet: Capsule Network with Self-Attention RoutingCode1
Benchmarking and scaling of deep learning models for land cover image classificationCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
Billion-scale semi-supervised learning for image classificationCode1
Function-Consistent Feature DistillationCode1
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Gated Attention Coding for Training High-performance and Efficient Spiking Neural NetworksCode1
Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple ModalitiesCode1
General E(2)-Equivariant Steerable CNNsCode1
BionoiNet: ligand-binding site classification with off-the-shelf deep neural networkCode1
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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