Kaggle Cats & Dogs Dataset

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Visualization of the Kaggle Cats & Dogs dataset on the Deep Lake UI

Kaggle Cats & Dogsdataset

What is Kaggle Cats & Dogs Dataset?

This Kaggle Cats & Dogs dataset is created to train machines to detect dogs and cats from the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). It can be used to build better and break-proof protections for web services. These protection systems are used to protect brute-force intrusion techniques, acts also as blog spam detector, etc.

Download Kaggle Cats & Dogs Dataset in Python

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

Load Kaggle Cats & Dogs Dataset Subset in Python

					import deeplake
ds = deeplake.load('hub://activeloop/kaggle-cats-dogs')

Kaggle Cats & Dogs Dataset Structure

Data Fields
  • images: tensor containing the image
  • labels: tensor containing labels of an corresponding image

How to use Kaggle Cats & Dogs Dataset with PyTorch and TensorFlow in Python

Train a model on Kaggle Cats & Dogs 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 Kaggle Cats & Dogs dataset with TensorFlow in Python
					dataloader = ds.tensorflow()

Additional Information about Kaggle Cats & Dogs Dataset

Kaggle Cats & Dogs Dataset Description

Kaggle Cats & Dogs 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!