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 14761500 of 10420 papers

TitleStatusHype
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
SAR Image Classification Based on Spiking Neural Network through Spike-Time Dependent Plasticity and Gradient DescentCode1
MLPerf Tiny BenchmarkCode1
Variational Quanvolutional Neural Networks with enhanced image encodingCode1
pix2rule: End-to-end Neuro-symbolic Rule LearningCode1
Partial success in closing the gap between human and machine visionCode1
The Backpropagation Algorithm Implemented on Spiking Neuromorphic HardwareCode1
Entropy-based Logic Explanations of Neural NetworksCode1
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight TransformersCode1
MlTr: Multi-label Classification with TransformerCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive LearningCode1
Scaling Vision with Sparse Mixture of ExpertsCode1
CLCC: Contrastive Learning for Color ConstancyCode1
Salient Positions based Attention Network for Image ClassificationCode1
Rethinking Transfer Learning for Medical Image ClassificationCode1
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features SelectionCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
Self-Supervised Learning with Data Augmentations Provably Isolates Content from StyleCode1
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
To Smooth or Not? When Label Smoothing Meets Noisy LabelsCode1
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive BiasCode1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Shuffle Transformer: Rethinking Spatial Shuffle for Vision TransformerCode1
Refiner: Refining Self-attention for Vision TransformersCode1
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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