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 68766900 of 10420 papers

TitleStatusHype
Enhanced Gradient for Differentiable Architecture Search0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations0
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation0
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised LearningCode0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification0
Transfer learning for automatic brain tumor classification Using MRI Images.0
Variational Knowledge Distillation for Disease Classification in Chest X-Rays0
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and BenchmarkCode0
ThanosNet: A Novel Trash Classification Method Using MetadataCode0
Implementation of Artificial Neural Networks for the Nepta-Uranian Interplanetary (NUIP) Mission0
ConViT: Improving Vision Transformers with Soft Convolutional Inductive BiasesCode0
Stride and Translation Invariance in CNNs0
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification0
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation0
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions0
HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification0
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm0
Learning Hyperbolic Representations of Topological FeaturesCode0
Learned Gradient Compression for Distributed Deep Learning0
Is it enough to optimize CNN architectures on ImageNet?Code0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Evolving parametrized Loss for Image Classification Learning on Small Datasets0
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video0
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified