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 8151–8175 of 10420 papers

TitleStatusHype
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural NetworksCode0
Outside the Box: Abstraction-Based Monitoring of Neural NetworksCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
Deep Learning based HEp-2 Image Classification: A Comprehensive Review—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Auto-Precision Scaling for Distributed Deep LearningCode0
Rethinking deep active learning: Using unlabeled data at model trainingCode0
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation ApproachCode0
Visualization approach to assess the robustness of neural networks for medical image classification—0
IC-Network: Efficient Structure for Convolutional Neural Networks—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
Feedback Control for Online Training of Neural Networks—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)—0
Learning Permutation Invariant Representations using Memory NetworksCode0
Crowd Counting via Segmentation Guided Attention Networks and Curriculum LossCode0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Fine-Grained Neural Architecture Search—0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Learning with Hierarchical Complement Objective—0
Coverage Testing of Deep Learning Models using Dataset Characterization—0
Selective sampling for accelerating training of deep neural networksCode0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Explanatory Masks for Neural Network Interpretability—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
10RevCol-HTop 1 Accuracy90—Unverified