CUB-200
Overview
The CUB-200-2011 dataset is designed for fine-grained bird species classification. It contains 11,788 images of 200 bird species, each labeled with its corresponding category. The dataset is widely used in fine-grained image classification and computer vision research. The data is split into 5,994 training images and 5,794 testing images.
Train: 5,994 images
Test: 5,794 images
Data Structure
When accessing an example using ds[i], you will receive a dictionary with the following keys:
Key |
Type |
Description |
|---|---|---|
|
|
H×W×3 RGB image |
|
int |
Class label (0-199) |
Usage Example
Basic Usage
from stable_datasets.images.cub200 import CUB200
# First run will download + prepare cache, then return the split as a HF Dataset
ds = CUB200(split="train")
# If you omit the split (split=None), you get a DatasetDict with all available splits
ds_all = CUB200(split=None)
sample = ds[0]
print(sample.keys()) # {"image", "label"}
# Optional: make it PyTorch-friendly
ds_torch = ds.with_format("torch")
References
Official website: https://www.vision.caltech.edu/datasets/cub_200_2011/
License: MIT License
Citation
@techreport{WahCUB_200_2011,
Title = {The Caltech-UCSD Birds-200-2011 Dataset},
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}}""",