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Datasets/CUB-200-2011

CUB-200-2011

Caltech-UCSD Birds-200-2011

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The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions.

Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

Benchmarks

3D/FID Colorization/PSNR@10 Colorization/PSNR@1 Colorization/PSNR@100 Concept-based Classification/Task Accuracy (%)Concept-based Classification/Concept Accuracy (%)Document Text Classification/Accuracy Error Understanding/Average highest confidence (ResNet-101)Error Understanding/Insertion AUC score (ResNet-101)Error Understanding/Average highest confidence (MobileNetV2)Error Understanding/Insertion AUC score (MobileNetV2)Error Understanding/Average highest confidence (EfficientNetV2-M)Error Understanding/Insertion AUC score (EfficientNetV2-M)Fine-Grained Image Classification/Accuracy Image Attribution/Insertion AUC score (ResNet-101)Image Attribution/Deletion AUC score (ResNet-101)Image Classification/Accuracy Image Classification/Task Accuracy (%)Image Classification/Concept Accuracy (%)Image Clustering/NMI Image Matching/Mean PCK@0.05 Image Matching/Mean PCK@0.1 Image Recognition/Accuracy Image Retrieval/R@1 Image Retrieval/R@2 Image Retrieval/R@4 Image Retrieval/R@8 Interpretable Machine Learning/Top 1 Accuracy Metric Learning/R@1 Multimodal Deep Learning/Accuracy Multimodal Text and Image Classification/Accuracy Object Localization/GT-known localization accuracy Object Localization/Top-1 Localization Accuracy Object Localization/average top-1 classification accuracy Reconstruction/FID Semantic correspondence/Mean PCK@0.05 Semantic correspondence/Mean PCK@0.1 Single-View 3D Reconstruction/FID Visual Recognition/Accuracy (%)Zero-Shot Learning/average top-1 classification accuracy Zero-Shot Learning/Accuracy Seen Zero-Shot Learning/Accuracy Unseen Zero-Shot Learning/H Zero-Shot Learning/Accuracy Zero-Shot Learning/Harmonic mean

Related Benchmarks

CUB-200-2011 (20 tasks) - 1 epoch/Continual Learning/Accuracy CUB-200-2011 (ResNet-101)/Error Understanding/Average highest confidence CUB-200-2011 (ResNet-101)/Error Understanding/Insertion AUC score CUB-200-2011 - 0-Shot/Few-Shot Image Classification/AP50 CUB-200-2011 - 0-Shot/Few-Shot Image Classification/Top-1 Accuracy CUB-200-2011 - 0-Shot/Image Classification/AP50 CUB-200-2011 - 0-Shot/Image Classification/Top-1 Accuracy CUB-200-2011 5-way (1-shot)/Few-Shot Image Classification/Accuracy CUB-200-2011 5-way (1-shot)/Image Classification/Accuracy CUB-200-2011 5-way (5-shot)/Few-Shot Image Classification/Accuracy CUB-200-2011 5-way (5-shot)/Image Classification/Accuracy CUB-200-2011, 30 samples per class/Image Classification/Accuracy CUB-200-2011, 5 samples per class/Image Classification/Accuracy

Statistics

Papers: 2,235
Benchmarks: 45

Links

Tasks

3D Bird Species Classification With Audio-Visual Data Colorization Concept-based Classification Cross-Domain Few-Shot Dataset Distillation - 1IPC Document Text Classification Error Understanding Few-Shot Class-Incremental Learning Few-Shot Image Classification Few-Shot Learning Fine-Grained Image Classification Fine-Grained Image Recognition Fine-Grained Visual Recognition Generalized Few-Shot Learning Generalized Zero-Shot Learning Graph Matching Image Attribution Image Classification Image Clustering Image Generation Image Matching Image Recognition Image Retrieval Interpretable Machine Learning Long-tail learning with class descriptors Metric Learning Multi-Modal Document Classification Multimodal Deep Learning Multimodal Text and Image Classification Object Localization Point-interactive Image Colorization Reconstruction Semantic correspondence Single-View 3D Reconstruction Small Data Image Classification Text-to-Image Generation Transductive Zero-Shot Classification Unsupervised Keypoint Estimation Visual Recognition Weakly-Supervised Object Localization Zero-Shot Learning