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 58515875 of 10420 papers

TitleStatusHype
Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution TrainingCode0
Refiner: Refining Self-attention for Vision TransformersCode1
Shuffle Transformer: Rethinking Spatial Shuffle for Vision TransformerCode1
Redundant representations help generalization in wide neural networksCode0
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive BiasCode1
Robust Implicit Networks via Non-Euclidean ContractionsCode0
Vision Transformers with Hierarchical AttentionCode1
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment0
An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification0
FedBABU: Towards Enhanced Representation for Federated Image ClassificationCode1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
RegionViT: Regional-to-Local Attention for Vision TransformersCode1
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamicsCode1
X-volution: On the unification of convolution and self-attention0
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric CancerCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Nonuniform Defocus Removal for Image Classification0
Stochastic Whitening Batch Normalization0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data0
A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer0
DynamicViT: Efficient Vision Transformers with Dynamic Token SparsificationCode1
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural NetworksCode0
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation0
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified