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Datasets/STL-10

STL-10

Self-Taught Learning 10

ImagesCustom (attribution + ImageNet license)Introduced 2011-01-01

The STL-10 is an image dataset derived from ImageNet and popularly used to evaluate algorithms of unsupervised feature learning or self-taught learning. Besides 100,000 unlabeled images, it contains 13,000 labeled images from 10 object classes (such as birds, cats, trucks), among which 5,000 images are partitioned for training while the remaining 8,000 images for testing. All the images are color images with 96×96 pixels in size.

Source: Unsupervised Feature Learning with C-SVDDNet Image Source: https://cs.stanford.edu/~acoates/stl10/

Benchmarks

Anomaly Detection/ROC AUCAnomaly Detection/AUC-ROCAutoML/Accuracy (%)AutoML/FLOPSAutoML/PARAMSContrastive Learning/Accuracy (Top-1)Fine-Grained Image Classification/AccuracyImage Classification/Percentage correctImage Classification/FLOPSImage Classification/PARAMSImage Classification/AccuracyImage Classification/Accuracy Image Clustering/AccuracyImage Clustering/NMIImage Clustering/ARIImage Clustering/Train SplitImage Clustering/BackboneImage Compression/Bit rateImage Generation/FIDImage Generation/Inception scoreImage Generation/Model Size (MB)Image Generation/RecallImage Generation/NFENeural Architecture Search/Accuracy (%)Neural Architecture Search/FLOPSNeural Architecture Search/PARAMSOut-of-Distribution Detection/Percentage correctSelf-Supervised Learning/AccuracySemi-Supervised Image Classification/AccuracyUnsupervised Anomaly Detection/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 1% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 10% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 20% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 30% anomaly/AUC-ROC

Related Benchmarks

STL-10 (1000 Labels, ImageNet-100 Unlabeled)/Image Classification/AccuracySTL-10 (1000 Labels, ImageNet-100 Unlabeled)/Semi-Supervised Image Classification/AccuracySTL-10, 1000 Labels/Image Classification/AccuracySTL-10, 1000 Labels/Semi-Supervised Image Classification/AccuracySTL-10, 40 Labels/Image Classification/AccuracySTL-10, 40 Labels/Semi-Supervised Image Classification/AccuracySTL-10, 5000 Labels/Image Classification/AccuracySTL-10, 5000 Labels/Semi-Supervised Image Classification/Accuracy

Statistics

Papers
1,092
Benchmarks
35

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Tasks

Anomaly DetectionAutoMLContrastive LearningFine-Grained Image ClassificationImage ClassificationImage ClusteringImage CompressionImage GenerationNeural Architecture SearchOut-of-Distribution DetectionSelf-Supervised LearningSemi-Supervised Image ClassificationUnsupervised Anomaly DetectionUnsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyUnsupervised Anomaly Detection with Specified Settings -- 1% anomalyUnsupervised Anomaly Detection with Specified Settings -- 10% anomalyUnsupervised Anomaly Detection with Specified Settings -- 20% anomalyUnsupervised Anomaly Detection with Specified Settings -- 30% anomalyUnsupervised Image Classification