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 6451–6475 of 10420 papers

TitleStatusHype
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning—0
ModelLock: Locking Your Model With a Spell—0
Learning from Exemplary Explanations—0
Learning from Crowds with Sparse and Imbalanced Annotations—0
Best Practices in Pool-based Active Learning for Image Classification—0
Demystifying What Code Summarization Models Learned—0
Demystifying Loss Functions for Classification—0
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks—0
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images—0
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification—0
Learning Fine-grained Features via a CNN Tree for Large-scale Classification—0
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration—0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification—0
Learning Expressive Prompting With Residuals for Vision Transformers—0
WheaCha: A Method for Explaining the Predictions of Models of Code—0
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches—0
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Lean classical-quantum hybrid neural network model for image classification—0
2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks—0
Learning efficient structured dictionary for image classification—0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding—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
10RevCol-HTop 1 Accuracy90—Unverified