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 9251–9275 of 10420 papers

TitleStatusHype
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients—0
FireCaffe: near-linear acceleration of deep neural network training on compute clusters—0
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning—0
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain—0
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)—0
Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture—0
Fisher Vectors Meet Neural Networks: A Hybrid Classification Architecture—0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems—0
FIXED: Frustratingly Easy Domain Generalization with Mixup—0
Fixed-Point Back-Propagation Training—0
Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs—0
Fixing the Teacher-Student Knowledge Discrepancy in Distillation—0
Fixing Weight Decay Regularization in Adam—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Flatten: Video Action Recognition is an Image Classification task—0
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification—0
FLOAT: FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS—0
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
Florida Wildlife Camera Trap Dataset—0
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels—0
FLuRKA: Fast and accurate unified Low-Rank & Kernel Attention—0
FLVoogd: Robust And Privacy Preserving Federated Learning—0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification—0
FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations—0
Focus-and-Expand: Training Guidance Through Gradual Manipulation of Input Features—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