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 11511200 of 10419 papers

TitleStatusHype
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
Counterfactual Generative NetworksCode1
Attentional Feature FusionCode1
DataMUX: Data Multiplexing for Neural NetworksCode1
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image ClassificationCode1
Convolutional Xformers for VisionCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
Attention-Based Second-Order Pooling Network for Hyperspectral Image ClassificationCode1
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural NetworksCode1
Attention-Challenging Multiple Instance Learning for Whole Slide Image ClassificationCode1
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing AttentionCode1
Convolution-enhanced Evolving Attention NetworksCode1
Decision Stream: Cultivating Deep Decision TreesCode1
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecksCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
Making Convolutional Networks Shift-Invariant AgainCode1
Asymmetric Polynomial Loss For Multi-Label ClassificationCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Deep Complex NetworksCode1
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial VehiclesCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
DeepMIM: Deep Supervision for Masked Image ModelingCode1
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and BeyondCode1
CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision ModelsCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Counterfactual Visual ExplanationsCode1
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep LearningCode1
Cross-modal Adversarial ReprogrammingCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
Deep Roto-Translation Scattering for Object ClassificationCode1
Deep Semantic Dictionary Learning for Multi-label Image ClassificationCode1
Automating Continual LearningCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue SegmentationCode1
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class ForgettingCode1
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable PrototypesCode1
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed DatasetsCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
AutoMix: Unveiling the Power of Mixup for Stronger ClassifiersCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
Asymmetric Loss For Multi-Label ClassificationCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
Controllable Orthogonalization in Training DNNsCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
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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