vl-datasets 0.0.4

Open, Clean Datasets for Computer Vision.

Open, Clean 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 collection of clean computer vision datasets, carefully analyzed and processed to avoid common image dataset issues such as:

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

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.

Join us in unlocking the full potential of our data and revolutionizing the industry!

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)	Sign up 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)	Sign up 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)	Sign up 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)	Sign up 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)	Sign up 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)	Sign up 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)	Sign up 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)	Sign up 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)	Sign up 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, you can 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 DataLoader.

train_dataset = CleanFood101('./', split='train', exclude_csv='food_101_vl-datasets_analysis.csv', transform=train_transform)
valid_dataset = CleanFood101('./', split='test', exclude_csv='food_101_vl-datasets_analysis.csv', transform=valid_transform)

Now you can use the dataset in a PyTorch training loop. Refer to our sample training notebooks for details.

Learn from Examples

	Dataset: CleanFood101 Framework: PyTorch. Description: Train a simple PyTorch model with the CleanFood101 dataset.
	Dataset: CleanPets Framework: fast.ai. Description: Train a simple 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.