CUB-200-2011

Caltech-UCSD Birds-200-2011

Dataset Information
Modalities
Images
Languages
Chinese
License
Unknown
Homepage
Official Website
Contents

Overview

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

Variants: CUB-200-2011, CUB-200-2011, 10 samples per class, CUB-200-2011 5-way (5-shot), CUB-200-2011 5-way (1-shot), Imbalanced CUB-200-2011, CUB-200-2011, 5 samples per class, CUB-200-2011, 30 samples per class, CUB-LT, CUB-200-2011 - 0-Shot, CUB-200 - 0-Shot Learning, CUB Birds, CUB 200 50-way (0-shot), CUB 200 5-way 5-shot, CUB 200 5-way 1-shot, CUB 128 x 128, CUB, CUB-200-2011

Associated Benchmarks

This dataset is used in 15 benchmarks:

Recent Benchmark Submissions

Task Model Paper Date
Fine-Grained Image Classification MDCM Multi-scale Activation, Refinement, and Aggregation: … 2025-04-12
Concept-based Classification EQ-CBM (ResNet-34) EQ-CBM: A Probabilistic Concept Bottleneck … 2024-09-22
Zero-Shot Learning ZeroDiff Exploring Data Efficiency in Zero-Shot … 2024-06-05
Zero-Shot Learning ZLaP* Label Propagation for Zero-shot Classification … 2024-04-05
Zero-Shot Learning ZLaP Label Propagation for Zero-shot Classification … 2024-04-05
Image Classification Sparse-CBM Sparse Concept Bottleneck Models: Gumbel … 2024-04-04
Image Attribution SMDL-Attribution (ICLR version) Less is More: Fewer Interpretable … 2024-02-14
Error Understanding SMDL-Attribution (ICLR version) Less is More: Fewer Interpretable … 2024-02-14
Zero-Shot Learning HDC-ZSC Zero-shot Classification using Hyperdimensional Computing 2024-01-30
Zero-Shot Learning HDC-ZSC-MLP Zero-shot Classification using Hyperdimensional Computing 2024-01-30
Interpretable Machine Learning Q-SENN Q-SENN: Quantized Self-Explaining Neural Networks 2023-12-21
Fine-Grained Image Classification ResNet-50 PCNN: Probable-Class Nearest-Neighbor Explanations Improve … 2023-08-25
Semantic correspondence LDM Correspondences Unsupervised Semantic Correspondence Using Stable … 2023-05-24
Zero-Shot Learning SPOT Synthetic Sample Selection for Generalized … 2023-04-06
Interpretable Machine Learning SLDD-Model Take 5: Interpretable Image Classification … 2023-03-23
Fine-Grained Image Classification HERBS Fine-grained Visual Classification with High-temperature … 2023-03-11
Metric Learning MS + DAS (K=8) DAS: Densely-Anchored Sampling for Deep … 2022-07-30
Zero-Shot Learning DUET DUET: Cross-modal Semantic Grounding for … 2022-07-04
Fine-Grained Image Classification IELT 0/1 Deep Neural Networks via … 2022-06-19
Image Attribution HSIC-Attribution Making Sense of Dependence: Efficient … 2022-06-13

Research Papers

Recent papers with results on this dataset:

External Links: