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 8701–8725 of 10420 papers

TitleStatusHype
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Doubly Convolutional Neural Networks—0
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference—0
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild—0
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures—0
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization—0
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling—0
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
Drift to Remember—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks—0
DropKey—0
DropKey for Vision Transformer—0
Dropout Distillation for Efficiently Estimating Model Confidence—0
Dropout Sampling for Robust Object Detection in Open-Set Conditions—0
Dropout with Expectation-linear Regularization—0
DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification—0
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning—0
DSM Refinement with Deep Encoder-Decoder Networks—0
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking—0
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition—0
Dual adaptive training of photonic neural networks—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