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 47264750 of 10420 papers

TitleStatusHype
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies0
Dynamic Mobile-Former: Strengthening Dynamic Convolution with Attention and Residual Connection in Kernel SpaceCode0
ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification0
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
Semantic-Aware Mixup for Domain GeneralizationCode0
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA0
A priori compression of convolutional neural networks for wave simulators0
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural NetworksCode0
SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration0
Neural Delay Differential Equations: System Reconstruction and Image Classification0
Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per classCode0
DartsReNet: Exploring new RNN cells in ReNet architecturesCode0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data0
Use the Detection Transformer as a Data AugmenterCode0
Are Visual Recognition Models Robust to Image Compression?0
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning0
Arithmetic Intensity Balancing Convolution for Hardware-aware Efficient Block Design0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning0
MC-MLP:Multiple Coordinate Frames in all-MLP Architecture for VisionCode0
Universal Semi-Supervised Learning for Medical Image ClassificationCode0
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks0
Can we learn better with hard samples?Code0
Meta-causal Learning for Single Domain Generalization0
PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift0
MULLER: Multilayer Laplacian Resizer for VisionCode0
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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