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 1005110075 of 10420 papers

TitleStatusHype
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual AttacksCode0
Universal Semi-Supervised Learning for Medical Image ClassificationCode0
The LogBarrier adversarial attack: making effective use of decision boundary informationCode0
SGD with Partial Hessian for Deep Neural Networks OptimizationCode0
SGAS: Sequential Greedy Architecture SearchCode0
Towards Partial Supervision for Generic Object Counting in Natural ScenesCode0
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection ChallengeCode0
Towards Physical Plausibility in Neuroevolution SystemsCode0
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and BenchmarkCode0
The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer VisionCode0
Towards Principled Design of Deep Convolutional Networks: Introducing SimpNetCode0
SEVEN: Pruning Transformer Model by Reserving SentinelsCode0
Selective Kernel NetworksCode0
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient DescentCode0
Segmenting two-dimensional structures with strided tensor networksCode0
The iMaterialist Fashion Attribute DatasetCode0
The Hitchhiker's Guide to Prior-Shift AdaptationCode0
SESS: Self-Ensembling Semi-Supervised 3D Object DetectionCode0
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionCode0
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced TrainingCode0
Towards Robust General Medical Image SegmentationCode0
Unsupervised Image Classification for Deep Representation LearningCode0
The Functional Neural ProcessCode0
Crowd Counting via Segmentation Guided Attention Networks and Curriculum LossCode0
Unsupervised Label Noise Modeling and Loss CorrectionCode0
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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