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

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Showing 1951–1975 of 10420 papers

TitleStatusHype
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone NetworksCode0
Learn What You Need in Personalized Federated LearningCode0
UV-SAM: Adapting Segment Anything Model for Urban Village IdentificationCode2
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image ModelsCode0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness—0
Activations and Gradients Compression for Model-Parallel TrainingCode0
Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor SearchCode0
Knee or ROC—0
A Strong Inductive Bias: Gzip for binary image classification—0
Image edge enhancement for effective image classification—0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability—0
Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification—0
Interpreting and Improving Attention From the Perspective of Large Kernel Convolution—0
Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image ClassificationCode2
Scissorhands: Scrub Data Influence via Connection Sensitivity in NetworksCode0
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification—0
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision ApplicationsCode4
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning—0
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain AlignmentCode0
Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer—0
Do Vision and Language Encoders Represent the World Similarly?Code1
Image classification network enhancement methods based on knowledge injection—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA Cats and Dogs Dataset—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