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 926950 of 10419 papers

TitleStatusHype
Generic-to-Specific Distillation of Masked AutoencodersCode1
Diagnosing Colorectal Polyps in the Wild with Capsule NetworksCode1
Contextual Diversity for Active LearningCode1
Automating Continual LearningCode1
GhostNet: More Features from Cheap OperationsCode1
AutoMix: Unveiling the Power of Mixup for Stronger ClassifiersCode1
GLiT: Neural Architecture Search for Global and Local Image TransformerCode1
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
Global Filter Networks for Image ClassificationCode1
Contextual Transformer Networks for Visual RecognitionCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Continual atlas-based segmentation of prostate MRICode1
Going deeper with Image TransformersCode1
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
Continual Hippocampus Segmentation with TransformersCode1
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path AggregationCode1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Continual Learning with Scaled Gradient ProjectionCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Gradient Centralization: A New Optimization Technique for Deep Neural NetworksCode1
Contrastive Deep SupervisionCode1
Gradient Projection Memory for Continual LearningCode1
GradInit: Learning to Initialize Neural Networks for Stable and Efficient TrainingCode1
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
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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