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 31763200 of 10420 papers

TitleStatusHype
Deep Online Probability Aggregation ClusteringCode0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
On the Viability of Monocular Depth Pre-training for Semantic SegmentationCode0
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configurationCode0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural NetworksCode0
MCA: Moment Channel Attention NetworksCode0
Bayesian Concept Bottleneck Models with LLM PriorsCode0
DeepOBS: A Deep Learning Optimizer Benchmark SuiteCode0
A Multi-task Supervised Compression Model for Split ComputingCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Deep Object Co-segmentation via Spatial-Semantic Network ModulationCode0
Adaptive Neuron-wise Discriminant Criterion and Adaptive Center Loss at Hidden Layer for Deep Convolutional Neural NetworkCode0
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesCode0
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network ArchitectureCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
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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