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 9051–9075 of 10420 papers

TitleStatusHype
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?Code0
Automatic Configuration of Deep Neural Networks with EGOCode0
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
Diagnosing Convolutional Neural Networks using their Spectral Response—0
Geometric Scattering for Graph Data Analysis—0
Deep convolutional Gaussian processesCode0
Learning Compressed Transforms with Low Displacement RankCode0
SNIP: Single-shot Network Pruning based on Connection SensitivityCode1
CINIC-10 is not ImageNet or CIFAR-10Code0
Ancient Coin Classification Using Graph Transduction Games—0
Target Aware Network Adaptation for Efficient Representation Learning—0
Accurate Dictionary Learning with Direct Sparsity ControlCode0
Extended Bit-Plane Compression for Convolutional Neural Network AcceleratorsCode0
CUNI System for the WMT18 Multimodal Translation Task—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksCode1
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification—0
Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Faster Training by Selecting Samples Using Embeddings—0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
Smooth Inter-layer Propagation of Stabilized Neural Networks for Classification—0
Weakly-Supervised Localization and Classification of Proximal Femur Fractures—0
Dropout Distillation for Efficiently Estimating Model Confidence—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified