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 11261150 of 10419 papers

TitleStatusHype
A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classificationCode1
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
Controllable Orthogonalization in Training DNNsCode1
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
Active Token MixerCode1
Convolutional Sequence to Sequence LearningCode1
Convolutional Xformers for VisionCode1
Convolution-enhanced Evolving Attention NetworksCode1
Contrastive Learning Improves Model Robustness Under Label NoiseCode1
Counterfactual Generative NetworksCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 DiagnosisCode1
Augmenting Convolutional networks with attention-based aggregationCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Augmented Neural ODEsCode1
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
Adaptive Token Sampling For Efficient Vision TransformersCode1
Cross-Layer Retrospective Retrieving via Layer AttentionCode1
Cross-modal Adversarial ReprogrammingCode1
Curriculum Temperature for Knowledge DistillationCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Asymmetric Loss For Multi-Label ClassificationCode1
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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