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Datasets/Caltech-101

Caltech-101

ImagesUnknownIntroduced 2004-01-01

The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels.

Source: Simple and Efficient Learning using Privileged Information

Benchmarks

Anomaly Detection/AUC (outlier ratio = 0.5)Density Estimation/Negative ELBODensity Estimation/NLLDensity Estimation/MMD-L2Density Estimation/COV-L2Fine-Grained Image Classification/Top-1 Error RateFine-Grained Image Classification/AccuracyImage Classification/Top-1 Error RateImage Classification/AccuracyImage Clustering/AccuracyImage Matching/IoUImage Matching/LT-ACCImage Matching/IoU (weak)Image Matching/LT-ACC (weak)Prompt Engineering/Harmonic meanSemantic correspondence/IoUSemantic correspondence/LT-ACCSemantic correspondence/IoU (weak)Semantic correspondence/LT-ACC (weak)Semi-Supervised Image Classification/AccuracyUnsupervised Anomaly Detection/AUC (outlier ratio = 0.5)Zero-Shot Learning/Accuracy

Related Benchmarks

Caltech-101, 202 Labels/Image Classification/AccuracyCaltech-101, 202 Labels/Semi-Supervised Image Classification/Accuracy

Statistics

Papers
709
Benchmarks
22

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Tasks

Anomaly DetectionDensity EstimationFine-Grained Image ClassificationImage ClassificationImage ClusteringImage MatchingPrompt EngineeringSemantic correspondenceSemi-Supervised Image ClassificationTransductive Zero-Shot ClassificationUnsupervised Anomaly DetectionZero-Shot Learning