Run CleanVision on Torchvision dataset

After you install these packages, you may need to restart your notebook runtime before running the rest of this notebook.

from torchvision.datasets import CIFAR10
from torch.utils.data import ConcatDataset
from cleanvision.imagelab import Imagelab

1. Download dataset and concatenate all splits

Since we’re interested in generally understanding what issues plague our data, we merge the training and test sets into one larger dataset before running CleanVision. You could alternatively just run the package on these two sets of data separately to obtain two different reports.

CIFAR10 is classification dataset, but CleanVision can be used to audit images from any type of dataset (including supervised or unsupervised learning).

Load all splits of the CIFAR10 dataset

train_set = CIFAR10(root="./", download=True)
test_set = CIFAR10(root="./", train=False, download=True)

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz

100%|██████████| 170498071/170498071 [00:03<00:00, 51842572.01it/s]

Extracting ./cifar-10-python.tar.gz to ./
Files already downloaded and verified

Concatenate train and test splits

dataset = ConcatDataset([train_set, test_set])

A sample from the dataset

dataset[0]

(<PIL.Image.Image image mode=RGB size=32x32>, 6)

Let’s look at the first image in this dataset

dataset[0][0]

2. Run CleanVision

imagelab = Imagelab(torchvision_dataset=dataset)

imagelab.find_issues()

Checking for dark, light, odd_aspect_ratio, low_information, exact_duplicates, near_duplicates, blurry, grayscale images ...

100%|██████████| 60000/60000 [00:43<00:00, 1389.65it/s]
100%|██████████| 60000/60000 [00:17<00:00, 3451.80it/s]

Issue checks completed. To see a detailed report of issues found, use imagelab.report().

3. View Results

Get a report of all the issues found

imagelab.report()

Issues found in order of severity in the dataset

|    | issue_type      |   num_images |
|---:|:----------------|-------------:|
|  0 | near_duplicates |           40 |
|  1 | dark            |           29 |
|  2 | light           |            3 |
|  3 | low_information |            1 |


Top 4 sets of images with near_duplicates issue
Set: 0

Set: 1

Set: 2

Set: 3

Top 4 examples with dark issue in the dataset.

Found 3 examples with light issue in the dataset.

Found 1 example with low_information issue in the dataset.

View more information about each image, such as what types of issues it exhibits and its quality score with respect to each type of issue.

imagelab.issues

	odd_aspect_ratio_score	is_odd_aspect_ratio_issue	low_information_score	is_low_information_issue	light_score	is_light_issue	grayscale_score	is_grayscale_issue	dark_score	is_dark_issue	blurry_score	is_blurry_issue	is_exact_duplicates_issue	is_near_duplicates_issue
0	1.0	False	0.854301	False	0.797130	False	1	False	0.915605	False	0.484206	False	False	False
1	1.0	False	0.926223	False	0.850131	False	1	False	0.976886	False	0.571952	False	False	False
2	1.0	False	0.818548	False	0.852996	False	1	False	1.000000	False	0.532279	False	False	False
3	1.0	False	0.813629	False	0.844033	False	1	False	0.775992	False	0.401225	False	False	False
4	1.0	False	0.898399	False	0.958406	False	1	False	0.919564	False	0.503716	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
59995	1.0	False	0.860407	False	0.794629	False	1	False	0.996078	False	0.523458	False	False	False
59996	1.0	False	0.888932	False	0.939203	False	1	False	0.843293	False	0.498186	False	False	False
59997	1.0	False	0.818150	False	0.960275	False	1	False	0.865067	False	0.444907	False	False	False
59998	1.0	False	0.900018	False	0.892104	False	1	False	0.952069	False	0.528622	False	False	False
59999	1.0	False	0.858985	False	0.809504	False	1	False	0.932046	False	0.501550	False	False	False

60000 rows × 14 columns

Get indices of all dark images in the dataset sorted by their dark score.

indices = imagelab.issues.query('is_dark_issue').sort_values(by='dark_score').index.tolist()

View the 5th darkest image in the dataset

dataset[indices[5]][0]

View global information about each issue, such as how many images in the dataset suffer from this issue.

imagelab.issue_summary

	issue_type	num_images
0	near_duplicates	40
1	dark	29
2	light	3
3	low_information	1
4	blurry	0
5	grayscale	0
6	odd_aspect_ratio	0
7	exact_duplicates	0

For more detailed guide on how to use CleanVision, check thetutorial notebook.