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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7476–7500 of 10420 papers

TitleStatusHype
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Deep Learning based HEp-2 Image Classification: A Comprehensive Review—0
SABLE: Secure And Byzantine robust LEarning—0
Backpropagation-free Training of Deep Physical Neural Networks—0
AMLA: an AutoML frAmework for Neural Network Design—0
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification—0
Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask—0
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation—0
Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs—0
Integrated perception with recurrent multi-task neural networks—0
Deep Learning Based Classification System For Recognizing Local Spinach—0
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT—0
Institutionally Distributed Deep Learning Networks—0
ParasNet: Fast Parasites Detection with Neural Networks—0
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution—0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
Predicting Out-of-Domain Generalization with Neighborhood Invariance—0
Predicting Stock Price Movement as an Image Classification Problem—0
Predicting the outputs of finite deep neural networks trained with noisy gradients—0
Amicable Aid: Perturbing Images to Improve Classification Performance—0
Predicting the Reliability of an Image Classifier under Image Distortion—0
Instant Adversarial Purification with Adversarial Consistency Distillation—0
Predicting Useful Neighborhoods for Lazy Local Learning—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified