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 1007610100 of 10420 papers

TitleStatusHype
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial NetworksCode0
An Aggregate Method for Thorax Diseases ClassificationCode0
Towards Sparsification of Graph Neural NetworksCode0
The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image ClassificationCode0
Spatially-sparse convolutional neural networksCode0
Spatially-Adaptive Filter Units for Deep Neural NetworksCode0
The Effectiveness of Data Augmentation in Image Classification using Deep LearningCode0
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of RobustnessCode0
Sequential Large Language Model-Based Hyper-parameter OptimizationCode0
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural NetworksCode0
With Friends Like These, Who Needs Adversaries?Code0
Towards Verifying Robustness of Neural Networks Against Semantic PerturbationsCode0
Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time PointCode0
Scaling the Scattering Transform: Deep Hybrid NetworksCode0
The Convolutional Tsetlin MachineCode0
When Do Neural Networks Outperform Kernel Methods?Code0
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image ClassificationCode0
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial ExamplesCode0
The Computational Limits of Deep LearningCode0
Unsupervised predictive coding models may explain visual brain representationCode0
Unsupervised representation learning with recognition-parametrised probabilistic modelsCode0
Unsupervised Representation Learning by Sorting SequencesCode0
Trading via Image ClassificationCode0
ThanosNet: A Novel Trash Classification Method Using MetadataCode0
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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