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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5176–5200 of 10420 papers

TitleStatusHype
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?Code1
Hierarchical Image Classification with A Literally Toy Dataset—0
Revealing and Protecting Labels in Distributed TrainingCode0
TorchXRayVision: A library of chest X-ray datasets and modelsCode2
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
Approximation properties of Residual Neural Networks for Kolmogorov PDEs—0
Dynamic Differential-Privacy Preserving SGD—0
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions—0
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning—0
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition ModelsCode0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width LimitCode0
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints—0
Explaining Latent Representations with a Corpus of ExamplesCode1
MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep LearningCode1
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian SpectrumsCode0
Towards artificial general intelligence via a multimodal foundation modelCode1
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationCode2
Diversity Matters When Learning From Ensembles—0
Physically Explainable CNN for SAR Image ClassificationCode1
Stable Anderson Acceleration for Deep LearningCode1
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
On sensitivity of meta-learning to support dataCode1
Can't Fool Me: Adversarially Robust Transformer for Video Understanding—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified