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HAM10000 Dataset

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Visualization of the HAM10000 dataset in the Deep Lake UI

HAM10000 dataset

What is HAM10000 Dataset?

The Human Against Machine with 10000 training images (HAM10000) dataset includes 10000 training images for detecting pigmented skin lesions. The images in the dataset were collected from different populations, acquired, and stored by different modalities. Cases included in the dataset include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than half of the lesions in the dataset are confirmed through histopathology, the ground truth for the rest of the cases is either follow-up examination, expert consensus, or confirmation by in-vivo confocal microscopy. The dataset includes lesions with multiple images.

Download HAM10000 Dataset in Python

Instead of downloading the HAM10000 dataset in Python, you can effortlessly load it in Python via our open-source package Deep Lake with just one line of code.

Load HAM10000 Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/ham10000")

HAM10000 Dataset Structure

HAM10000 Data Fields
  • images: tensor containing the face image.
  • genders: tensor contains different genders.
  • lesion_categories: tensor to represent different lesion categories.
  • sources: tensor to represent the source.
  • localizations: tensor to represent the localization of the lesion.
  • ages: tensor representing the age of a person.
  • lesion_ids: tensor representing lesion id.
  • image_ids: tensor representing image id.
HAM10000 Data Splits

How to use HAM10000 Dataset with PyTorch and TensorFlow in Python

Train a model on HAM10000 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 HAM10000 dataset with TensorFlow in Python
				
					dataloader = ds.tensorflow()

Additional Information about HAM10000 Dataset

HAM10000 Dataset Description

  • Repository: N/A
  • Paper: Tschandl, Philipp, 2018, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions”, https://doi.org/10.7910/DVN/DBW86T,
  • Point of Contact: N/A
HAM10000 Dataset Curators

Tschandl, Philipp

HAM10000 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!
HAM10000 Dataset Citation Information
				
					@inproceedings{,
  title = {The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
  author = {Tschandl, Philipp},
    year = {2018} 
}

HAM10000 Dataset FAQs

What is the HAM10000 dataset for Python?

The HAM10000 dataset is a popular benchmark dataset that can be used for machine learning and for comparing machine learning results with human experts. The cases included in the HAM10000 contain a representative collection of all important diagnostic categories in the realm of pigmented lesions.

How to download the HAM10000 dataset in Python?

You can load the HAM10000 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 HAM10000 dataset training subset in Python.

How can I use HAM10000 dataset in PyTorch or TensorFlow?

You can stream the HAM10000 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 HAM10000 dataset with PyTorch in Python or train a model on the HAM10000 dataset with TensorFlow in Python.

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