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 20762100 of 10420 papers

TitleStatusHype
A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningCode1
Automated Relational Meta-learningCode1
Towards Accurate Post-training Network Quantization via Bit-Split and StitchingCode1
Understanding Contrastive Representation Learning through Geometry on the HypersphereCode1
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
PolSF: PolSAR image dataset on San FranciscoCode1
Image Classification with Deep Learning in the Presence of Noisy Labels: A SurveyCode1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature LearningCode1
Addressing Failure Detection by Learning Model ConfidenceCode1
Transform-Invariant Convolutional Neural Networks for Image Classification and SearchCode1
GhostNet: More Features from Cheap OperationsCode1
CSPNet: A New Backbone that can Enhance Learning Capability of CNNCode1
Rigging the Lottery: Making All Tickets WinnersCode1
Universal Adversarial Robustness of Texture and Shape-Biased ModelsCode1
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation AnchoringCode1
Beyond Synthetic Noise: Deep Learning on Controlled Noisy LabelsCode1
General E(2)-Equivariant Steerable CNNsCode1
Self-training with Noisy Student improves ImageNet classificationCode1
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Soft-Label Dataset Distillation and Text Dataset DistillationCode1
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time SystemsCode1
Addressing Failure Prediction by Learning Model ConfidenceCode1
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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