
Visualization of the Animal10n dataset in the Deep Lake UI
ANIMAL10N dataset contains ten classes of animals with a total of 50,000 training and testing pictures. The following animals are included in the dataset: lynx, guinea pig, jaguar, cat, hamster, cheetah, coyote, chimpanzee, wolf, and orangutan. Noisy labels were introduced spontaneously by human error. The estimated noise rate is 8%.
Instead of downloading the ANIMAL10N dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.
import deeplake
ds = deeplake.load("hub://activeloop/animal10n-train")
import deeplake
ds = deeplake.load("hub://activeloop/animal10n-test")
ANIMAL10N Data Fields
- images: tensor containing the image.
- labels: tensor to represent the category of animal in the image.
ANIMAL10N Data Splits
Train a model on ANIMAL10N 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 Animal10n dataset with TensorFlow in Python
dataloader = ds.tensorflow()
- Homepage: https://dm.kaist.ac.kr/datasets/animal-10n/
- Repository: N/A
- Paper: Introduced by Song, Hwanjun and Kim, Minseok and Lee, Jae-Gil. in {SELFIE}: Refurbishing Unclean Samples for Robust Deep Learning
- Point of Contact: N/A
Animal10n Dataset Curators
Song, Hwanjun and Kim,Minseok and Lee, Jae-Gil.
ANIMAL10N Dataset Licensing Information
ANIMAL10N Dataset Citation Information
@inproceedings{song2019selfie,
title={{SELFIE}: Refurbishing Unclean Samples for Robust Deep Learning},
author={Song, Hwanjun and Kim, Minseok and Lee, Jae-Gil},
booktitle={ICML},
year={2019} }
What classes does ANIMAL10N dataset contain?
- lynx
- guinea pig
- jaguar
- cat
- hamster
- cheetah
- coyote
- chimpanzee
- wolf
- orangutan
What is the ANIMAL10N dataset for Python?
There are ten classes with a total of 50,000 training and testing pictures. Please note that in ANIMAL10N, noisy labels were introduced spontaneously by human error, with an estimated noise rate of 8%.
How to download the ANIMAL10N dataset in Python?
You can load the ANIMAL10N 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 ANIMAL10N dataset training subset and Animal10N testing subset in Python.
How can I use the ANIMAL10N dataset in PyTorch or TensorFlow?
You can stream the ANIMAL10N 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 ANIMAL10N dataset with PyTorch in Python or train a model on the ANIMAL10N dataset with TensorFlow in Python.