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 61516175 of 10420 papers

TitleStatusHype
Document AI: Benchmarks, Models and Applications0
Learning with convolution and pooling operations in kernel methods0
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension0
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding0
Learning Data Teaching Strategies Via Knowledge Tracing0
Image Classification with Consistent Supporting Evidence0
Full-attention based Neural Architecture Search using Context Auto-regression0
Nonlinear Tensor Ring Network0
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification0
A Relational Model for One-Shot Classification0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets0
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget0
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition0
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification0
Virus-MNIST: Machine Learning Baseline Calculations for Image Classification0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems0
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Hierarchical Image Classification with A Literally Toy Dataset0
Revealing and Protecting Labels in Distributed TrainingCode0
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation0
Approximation properties of Residual Neural Networks for Kolmogorov PDEs0
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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
10RevCol-HTop 1 Accuracy90Unverified