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 5801–5825 of 10420 papers

TitleStatusHype
Detecting Adversarial Examples in Convolutional Neural Networks—0
Neural Network Layer Algebra: A Framework to Measure Capacity and Compression in Deep Learning—0
Learning The Structure of Deep Convolutional Networks—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification—0
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces—0
Learning to aggregate feature representations—0
Learning to be Global Optimizer—0
Attending Category Disentangled Global Context for Image Classification—0
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Learning To Collaborate in Decentralized Learning of Personalized Models—0
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks—0
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification—0
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability—0
Learning to Complement with Multiple Humans—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—0
Learning to Detect Malicious Clients for Robust Federated Learning—0
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification—0
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks—0
Coverage Testing of Deep Learning Models using Dataset Characterization—0
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review—0
Learning to Generate Images with Perceptual Similarity Metrics—0
Machine Learning for Brain Disorders: Transformers and Visual Transformers—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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
10RevCol-HTop 1 Accuracy90—Unverified