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 651675 of 10419 papers

TitleStatusHype
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Deep Fried ConvnetsCode1
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image ClassificationCode1
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image ClassificationCode1
Deep Transferring QuantizationCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
Adaptive Edge Offloading for Image Classification Under Rate LimitCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and BeyondCode1
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image RecognitionCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
Decoupled Weight Decay RegularizationCode1
Deep AutoAugmentCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Augmented Neural ODEsCode1
Deep convolutional tensor networkCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Augmenting Convolutional networks with attention-based aggregationCode1
A Unified Algebraic Perspective on Lipschitz Neural NetworksCode1
Automated Learning Rate Scheduler for Large-batch TrainingCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Deep Hyperspectral Unmixing using Transformer NetworkCode1
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecksCode1
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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