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 11761200 of 10419 papers

TitleStatusHype
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
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