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 16511675 of 10419 papers

TitleStatusHype
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image EncodingCode1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation LearningCode1
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised DetectionCode1
Concept Learners for Few-Shot LearningCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image RecognitionCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
Multimodal Fusion Transformer for Remote Sensing Image ClassificationCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image ClassificationCode1
Capsules with Inverted Dot-Product Attention RoutingCode1
Multi-Scale Dense Networks for Resource Efficient Image ClassificationCode1
Carrying out CNN Channel Pruning in a White BoxCode1
Multiscale Vision TransformersCode1
MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide ImagesCode1
Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray ImagesCode1
DC3DCD: unsupervised learning for multiclass 3D point cloud change detectionCode1
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
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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