Documentation#
CleanVision automatically detects various issues in image datasets, such as images that are: (near) duplicates, blurry, over/under-exposed, etc. This data-centric AI package is designed as a quick first step for any computer vision project to find problems in your dataset, which you may want to address before applying machine learning.
Installation#
To install the latest stable version (recommended):
$ pip install cleanvision
To install the bleeding-edge developer version:
$ pip install git+https://github.com/cleanlab/cleanvision.git
To install with HuggingFace optional dependencies
$ pip install "cleanvision[huggingface]"
To install with Torchvision optional dependencies
$ pip install "cleanvision[pytorch]"
Quickstart#
Using CleanVision to audit your image data is as simple as running the code below:
from cleanvision.imagelab import Imagelab
# Specify path to folder containing the image files in your dataset
imagelab = Imagelab(data_path="FOLDER_WITH_IMAGES/")
# Automatically check for a predefined list of issues within your dataset
imagelab.find_issues()
# Produce a neat report of the issues found in your dataset
imagelab.report()
CleanVision diagnoses many types of issues, but you can also check for only specific issues:
issue_types = {"light": {}, "blurry": {}}
imagelab.find_issues(issue_types)
# Produce a report with only the specified issue_types
imagelab.report(issue_types.keys())
Run CleanVision on a Hugging Face dataset
from datasets import load_dataset, concatenate_datasets
# Download and concatenate different splits
dataset_dict = load_dataset("cifar10")
dataset = concatenate_datasets([d for d in dataset_dict.values()])
# Specify the key for Image feature in dataset.features in `image_key` argument
imagelab = Imagelab(hf_dataset=dataset, image_key="img")
imagelab.find_issues()
imagelab.report()
Run CleanVision on a Torchvision dataset
from torchvision.datasets import CIFAR10
from torch.utils.data import ConcatDataset
# Download and concatenate train set and test set
train_set = CIFAR10(root="./", download=True)
test_set = CIFAR10(root="./", train=False, download=True)
dataset = ConcatDataset([train_set, test_set])
imagelab = Imagelab(torchvision_dataset=dataset)
imagelab.find_issues()
imagelab.report()
More on how to get started with CleanVision: