NABirds Dataset

Estimated reading: 3 minutes 1711 views

Visualization of the NABirds dataset in the Deep Lake UI

NABirds Dataset

What is NABirds Dataset?

The NABirds dataset is a collection of 48,000 annotated photographs of the 400 species of birds that are commonly seen across North America. In the dataset, there are more than 100 photographs for each species, including separate annotations for males, females, and juveniles that comprise 700 visual categories

Download NABirds Dataset in Python

Instead of downloading the NABirds dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load NABirds Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/nabirds-dataset-train')

Load NABirds Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/nabirds-dataset-val')

NABirds Dataset Structure

NABirds Data Fields

images: tensor containing the face image.
labels: tensor containing labels of bird categories.
boxes: tensor to localize the bird in the image.

NABirds Data Splits

The NABirds dataset training set is composed of 23938.
The NABirds dataset validation set is composed of 24633.

How to use NABirds Dataset with PyTorch and TensorFlow in Python

Train a model on the NABirds dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on the NABirds dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about NABirds Dataset

NABirds Dataset Description

Homepage: https://dl.allaboutbirds.org/nabirds
Repository: N/A
Paper: Introduced by Grant Van Horn et al. in Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection
Point of Contact: [email protected]

NABirds Dataset Curators

Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Serge Belongie

NABirds Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

NABirds Dataset Citation Information

				
					@inproceedings{,
  title = {Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection},
  author = {Horn},
}

NABirds Dataset FAQs

What is the NABirds dataset for Python?

NABirds is a collection of 48,000 annotated photographs of 400 species of birds commonly observed in North America. This dataset is often used for fine-grained visual categorization experiments.

How to download the NABirds dataset in Python?

You can load the NABirds dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load the NABirds dataset training subset or NABirds validation subset in Python.

How can I use the NABirds dataset in PyTorch or TensorFlow?

You can stream the NABirds dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on the NABirds dataset with PyTorch in Python or train a model on the NABirds dataset with TensorFlow in Python.