| --- |
| license: mit |
| task_categories: |
| - image-classification |
| language: |
| - en |
| tags: |
| - biology |
| - birds |
| - fine-grained image classification |
| - natural language description |
| size_categories: |
| - 1K<n<10K |
| --- |
| # Dataset Card for CUB_200_2011 |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** |
| https://www.vision.caltech.edu/datasets/cub_200_2011/ |
| - **Citation:** |
| @techreport{WahCUB_200_2011, |
| Title = , |
| Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.}, |
| Year = {2011} |
| Institution = {California Institute of Technology}, |
| Number = {CNS-TR-2011-001} |
| } |
| ### Dataset Summary |
| |
| The Caltech-UCSD Birds 200-2011 dataset (CUB-200-2011) is an extended version of the original CUB-200 dataset, featuring photos of 200 bird species primarily from North America. This 2011 version collects detailed natural language descriptions for each image through Amazon Mechanical Turk (AMT). |
|
|
| ### How to Use |
| ``` |
| from datasets import load_dataset |
| CUB_200 = load_dataset("KAKIZHOU/CUB-200") |
| ``` |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| This dataset can support a variety of computer vision tasks, including but not limited to: |
|
|
| * Fine-Grained Image Classification |
| * Object Detection and Localization |
| * Semantic Segmentation |
| * Attribute-Based Recognition |
| * Multitask Learning |
|
|
| ### Languages |
|
|
| The dataset includes annotations in English |
|
|
| ### Data Fields |
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|
| * images: Photographs of birds across 200 species. |
| * annotations: This includes: |
| * bounding boxes: Specify the bird's location within the image. |
| * segmentation labels: Provide pixel-wise segmentation for precise object segmentation. |
| * part locations: 15 specific parts of the bird are annotated for detailed analysis. |
| * binary attributes: 312 attributes indicating the presence or absence of certain features or behaviors. |
| * natural language descriptions: Ten single-sentence descriptions per image, collected via AMT. |
|
|
|
|
| ### Data Splits |
|
|
| * Training set: 8,855 images |
| * Test set: 2,933 images |
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