vl-datasets 0.0.6

Open, Clean Datasets for Computer Vision.

Open, Clean, Curated Datasets for Computer Vision

🔥 We use fastdup - a free tool to clean all datasets shared in this repo.
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Description

vl-datasets is a Python package that provides access to clean computer vision datasets with only 2 lines of code.

For example, to get access to the clean version of the Food-101 dataset simply run:

We support some of the most widely used computer vision datasets. Let us know if you have additional request to support a new dataset.

All the datasets are analyzed for issues such as:

• Duplicates.
• Broken images.
• Outliers.
• Dark/Bright/Blurry images.

vl-datasets provides a convenient way to access these cleaned datasets in Python.

Alternatively, for each dataset in this repo, we provide a .csv file that lists the problematic images from the dataset.

You can use the listed images in the .csv to improve the model by re-labeling the them or just simply remove it from the dataset.

Why?

Computer vision is an exciting and rapidly advancing field, with new techniques and models emerging now and then. However, to develop and evaluate these models, it's essential to have reliable and standardized datasets to work with.

Even with the recent success of generative models, data quality remains an issue that's mainly overlooked. Training models will erroneours data impacts model accuracy, incurs costs in time, storage and computational resources.

We believe that access to clean and high-quality computer vision datasets leads to accurate, non-biased, and efficient model. By providing public access to vl-datasets we hope it helps advance the field of computer vision.

Datasets & Access

We're a startup and we'd like to offer free access to the datasets as much as we can afford to. But in doing so, we'd also need your support.

We're offering select .csv files completely free with no strings attached. For access to our complete dataset and exclusive beta features, all we ask is that you sign up to be a beta tester – it's completely free and your feedback will help shape the future of our platform.

Here is a table of widely used computer vision datasets, issues we found and a link to access the .csv file.

Dataset	Issues (WIP)	CSV
Food-101	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Download here.
Oxford-IIIT Pet	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Download here.
Imagenette	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Download here.
LAION-1B	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
Imagenet-21k	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
Imagenet-1k	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
KITTI	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
DeepFashion	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
Places365	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
CelebA-HQ	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
ADE20K	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.
COCO	Duplicates - 0.24% (12,345) Outliers - 0.85% (456) Broken - 0.85% (456) Blur - 0.85% (456) Dark - 0.85% (456) Bright - 0.85% (456)	Request access here.

Learn more on how we clean the datasets using our profilling tool here.

Installation

Option 1 - Install vl_datasets package from PyPI:

pip install vl-datasets

Option 2 - Install the bleeding edge version on GitHub:

pip install git+https://github.com/visual-layer/vl-datasets.git@main --upgrade

Usage

To start using vl-datasets, import the clean version of the dataset with:

from vl_datasets import CleanFood101

This should import the clean version of the Food101 dataset.

Next, you can load the dataset as a PyTorch Dataset.

train_dataset = CleanFood101('./', split='train')
valid_dataset = CleanFood101('./', split='test')

If you have a custom .csv file you can optionally pass in the file:

train_dataset = CleanFood101('./', split='train', exclude_csv='my-file.csv')

The filenames listed in the .csv will be excluded in the dataset.

Next, you can load the train and validation datasets in a PyTorch training loop.

See the Learn from Examples section to learn more.

NOTE: Sign up here for free to be our beta testers and get full access to the all the .csv files for the dataset listed in this repo.

With the dataset loaded you can train a model using PyTorch training loop.

Learn from Examples

	Dataset: CleanFood101 Framework: PyTorch. Description: Load a dataset and train a PyTorch model.
	Dataset: CleanOxfordIIITPet Framework: fast.ai. Description: Finetune a pretrained TIMM model using fastai.

License

vl-datasets is licensed under the Apache 2.0 License. See LICENSE.

However, you are bound to the usage license of the original dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. We provide no warranty or guarantee of accuracy or completeness.

Getting Help

Get help from the Visual Layer team or community members via the following channels -

• Slack.
• GitHub issues.
• Discussion forum.

About Visual-Layer

Visual Layer is founded by the authors of XGBoost, Apache TVM & Turi Create - Danny Bickson, Carlos Guestrin and Amir Alush.

Learn more about Visual Layer here.