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 92519275 of 10420 papers

TitleStatusHype
Causal importance of orientation selectivity for generalization in image recognitionCode0
Inference via Sparse Coding in a Hierarchical Vision ModelCode0
Efficient Ladder-style DenseNets for Semantic Segmentation of Large ImagesCode0
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classificationCode0
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A SurveyCode0
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network ModelsCode0
Multi-Modal Adapter for Vision-Language ModelsCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Multimodal Adaptive Inference for Document Image Classification with Anytime Early ExitingCode0
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical DecisionsCode0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
CATALOG: A Camera Trap Language-guided Contrastive Learning ModelCode0
Saliency Guided Experience Packing for Replay in Continual LearningCode0
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial RobustnessCode0
Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?Code0
CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram ClassificationCode0
Initialization Matters for Adversarial Transfer LearningCode0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
Multi-Modal Fusion by Meta-InitializationCode0
Capsule Routing via Variational BayesCode0
Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?Code0
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Show:102550
← PrevPage 371 of 417Next →

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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified