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 11511175 of 10419 papers

TitleStatusHype
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
Attentional-Biased Stochastic Gradient DescentCode1
Attentional Feature FusionCode1
Auto Learning AttentionCode1
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image ClassificationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural NetworksCode1
CrAM: A Compression-Aware MinimizerCode1
Dataset Condensation with Contrastive SignalsCode1
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution EnvironmentsCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagationCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
Asymmetric Loss For Multi-Label ClassificationCode1
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image RecognitionCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Decision Stream: Cultivating Deep Decision TreesCode1
Contrastive Deep SupervisionCode1
Decoupled Weight Decay RegularizationCode1
Deep AutoAugmentCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
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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