Dataset Linting Tutorial#

Problem Statement#

Exploratory data analysis (EDA) can be overwhelming. There are so many things to check. Duplicates in your dataset, bad/corrupted images in the set, blurred or bright/dark images, the list goes on.

DataEval created a Linting class to assist you with your EDA so you can start training your models on high quality data.

When to use#

The Linting class should be used during the initial EDA process or if you are trying to verify that you have the right data in your dataset.

What you will need#

A dataset to analyze
A python environment with the following packages installed:
- dataeval[torch] or dataeval[all]
- torchvision

Getting Started#

Let’s import the required libraries needed to set up a minimal working example

import numpy as np
import torch
import torchvision.datasets as datasets
import torchvision.transforms.v2 as v2

from dataeval.detectors.linters import Outliers

Loading in the data#

We are going to start by loading in torchvision’s CIFAR-10 dataset.

The CIFAR-10 dataset contains 60,000 images - 50,000 in the train set and 10,000 in the test set. For the purposes of this demonstration, we are just going to use the test set.

# Load in the cifar-10 dataset from torchvision
to_tensor = v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])
testing_dataset = datasets.CIFAR10("./data", train=False, download=True, transform=to_tensor)
test_data = np.array(testing_dataset.data, dtype=float)

Files already downloaded and verified

Linting the Dataset#

Now we can begin finding those images which are significantly different from the rest of the data.

# Initialize the Duplicates class
outliers = Outliers()

# Evaluate the data
results = outliers.evaluate(test_data)

/dataeval/src/dataeval/_internal/detectors/outliers.py:59: RuntimeWarning: invalid value encountered in divide
  mod_z_score = 0.6745 * abs_diff / med_abs_diff

The results are a dictionary with the keys being the image that has an issue in one of the listed properties below:

Brightness
Blurriness
Missing
Zero
Width
Height
Size
Aspect Ratio
Channels
Depth

print(f"Total number of images with an issue: {len(results.issues)}")

Total number of images with an issue: 1197

# Show each image that has at least one issue
for image, issue in results.issues.items():
    print(f"{image} - {issue}")

- {'kurtosis': 2.695}
- {'skew': -2.49, 'kurtosis': 4.53, 'entropy': 3.182, 'darkness': 0.937}
- {'kurtosis': 1.692, 'contrast': 3.293}
- {'kurtosis': 2.234}
- {'entropy': 4.227}
- {'contrast': 3.352}
- {'sharpness': 95.75}
- {'kurtosis': 1.848}
- {'kurtosis': 2.037}
- {'sharpness': 112.4}
- {'entropy': 4.01}
- {'contrast': 3.55}
- {'sharpness': 93.06}
- {'skew': -4.293, 'kurtosis': 20.39, 'entropy': 3.14}
- {'entropy': 3.068}
- {'contrast': 3.371}
- {'entropy': 3.799}
- {'kurtosis': 3.941}
- {'var': 0.1293}
- {'sharpness': 91.9}
- {'sharpness': 103.75}
- {'kurtosis': 2.031, 'entropy': 3.957, 'contrast': 3.727}
- {'sharpness': 114.44}
- {'sharpness': 91.44}
- {'kurtosis': 3.242}
- {'sharpness': 107.44}
- {'entropy': 3.676}
- {'kurtosis': 2.148}
- {'entropy': 3.773}
- {'sharpness': 90.56}
- {'kurtosis': 2.217}
- {'darkness': 0.7725}
- {'kurtosis': 2.105, 'darkness': 0.757}
- {'entropy': 3.602}
- {'mean': 0.932, 'kurtosis': 2.393, 'entropy': 2.914, 'darkness': 0.89}
- {'var': 0.1276}
- {'kurtosis': 5.15}
- {'kurtosis': 1.809, 'entropy': 4.246}
- {'sharpness': 111.0}
- {'skew': 2.139, 'kurtosis': 3.568, 'entropy': 4.184, 'contrast': 3.695}
- {'kurtosis': 1.927}
- {'darkness': 0.745}
- {'kurtosis': 1.661}
- {'contrast': 3.498}
- {'sharpness': 93.1}
- {'kurtosis': 1.705}
- {'kurtosis': 4.496}
- {'entropy': 4.105}
- {'kurtosis': 3.848}
- {'skew': -3.49, 'kurtosis': 17.45, 'entropy': 4.043, 'darkness': 0.8276}
- {'contrast': 3.37}
- {'entropy': 4.168}
- {'sharpness': 103.3}
- {'sharpness': 110.56}
- {'skew': -2.088, 'kurtosis': 3.064, 'entropy': 3.736, 'darkness': 0.9097}
- {'mean': 0.913, 'skew': -2.322, 'kurtosis': 4.94, 'entropy': 2.33, 'darkness': 0.953}
- {'sharpness': 115.0}
- {'sharpness': 107.75}
- {'sharpness': 100.1}
- {'kurtosis': 2.383, 'contrast': 3.31}
- {'kurtosis': 2.963, 'contrast': 3.363}
- {'kurtosis': 3.373}
- {'kurtosis': 2.193, 'entropy': 3.44}
- {'entropy': 4.03}
- {'sharpness': 92.5}
- {'sharpness': 104.1}
- {'kurtosis': 2.11}
- {'skew': -2.994, 'kurtosis': 8.2, 'entropy': 3.906, 'darkness': 0.82}
- {'kurtosis': 2.127}
- {'entropy': 3.564}
- {'kurtosis': 1.853}
- {'darkness': 0.7373}
- {'sharpness': 91.6}
- {'kurtosis': 2.94}
- {'sharpness': 96.6}
- {'var': 0.1296, 'entropy': 4.027}
- {'kurtosis': 1.901}
- {'kurtosis': 2.312, 'entropy': 4.07, 'darkness': 0.784}
- {'kurtosis': 1.682}
- {'sharpness': 94.8}
- {'sharpness': 92.0}
- {'kurtosis': 2.219}
- {'kurtosis': 6.97}
- {'entropy': 4.23}
- {'kurtosis': 2.36}
- {'kurtosis': 2.75}
- {'skew': -2.049, 'kurtosis': 3.0, 'entropy': 3.863, 'darkness': 0.784}
- {'kurtosis': 3.178}
- {'kurtosis': 2.283}
- {'entropy': 4.156}
- {'sharpness': 113.0}
- {'kurtosis': 2.1, 'entropy': 4.215}
- {'sharpness': 90.56}
- {'entropy': 3.73}
- {'entropy': 3.846, 'sharpness': 106.25}
- {'kurtosis': 3.83}
- {'sharpness': 110.56}
- {'sharpness': 107.25}
- {'var': 0.1334}
- {'kurtosis': 1.938}
- {'sharpness': 107.75}
- {'kurtosis': 1.774}
- {'kurtosis': 2.12}
- {'skew': -4.297, 'kurtosis': 23.66, 'entropy': 3.166}
- {'kurtosis': 2.273, 'entropy': 3.67, 'contrast': 4.01}
- {'entropy': 3.377}
- {'kurtosis': 2.164}
- {'sharpness': 114.1}
- {'entropy': 3.762}
- {'kurtosis': 3.682, 'contrast': 3.32}
- {'entropy': 4.04}
- {'kurtosis': 2.03}
- {'entropy': 4.19, 'darkness': 0.7793}
- {'sharpness': 109.25}
- {'kurtosis': 3.172}
- {'kurtosis': 1.876}
- {'kurtosis': 3.2}
- {'skew': -2.88, 'kurtosis': 8.836, 'entropy': 3.883}
- {'kurtosis': 2.537}
- {'kurtosis': 3.643}
- {'var': 0.1497, 'entropy': 3.568}
- {'sharpness': 100.9}
- {'skew': -2.193, 'kurtosis': 4.113, 'entropy': 4.152, 'darkness': 0.7646}
- {'kurtosis': 2.693}
- {'kurtosis': 1.847}
- {'sharpness': 99.44}
- {'kurtosis': 2.617}
- {'kurtosis': 2.314}
- {'sharpness': 103.2}
- {'kurtosis': 1.615, 'entropy': 3.773}
- {'kurtosis': 2.959, 'entropy': 4.023}
- {'mean': 0.925, 'skew': -2.672, 'kurtosis': 5.594, 'entropy': 2.037, 'darkness': 0.992}
- {'entropy': 4.21}
- {'zeros': 0.0625}
- {'sharpness': 100.7}
- {'sharpness': 92.0}
- {'entropy': 3.451}
- {'contrast': 3.352}
- {'kurtosis': 2.682}
- {'entropy': 3.918}
- {'kurtosis': 2.094}
- {'kurtosis': 1.681}
- {'mean': 0.8755, 'kurtosis': 2.111, 'entropy': 3.973, 'darkness': 0.816}
- {'sharpness': 95.6}
- {'kurtosis': 2.768}
- {'entropy': 4.227}
- {'kurtosis': 1.851}
- {'kurtosis': 1.7295}
- {'contrast': 3.521}
- {'skew': -1.875, 'kurtosis': 4.125}
- {'sharpness': 96.06}
- {'entropy': 4.152}
- {'var': 0.1301}
- {'entropy': 3.756}
- {'sharpness': 119.8}
- {'kurtosis': 2.832}
- {'entropy': 4.215, 'sharpness': 103.7}
- {'kurtosis': 1.653}
- {'skew': -1.934, 'kurtosis': 3.129}
- {'skew': -1.89, 'kurtosis': 1.841, 'entropy': 3.389, 'darkness': 0.9253}
- {'entropy': 3.49, 'darkness': 0.757}
- {'kurtosis': 1.93, 'entropy': 3.842}
- {'entropy': 4.2}
- {'entropy': 3.676}
- {'kurtosis': 2.027}
- {'kurtosis': 2.447}
- {'sharpness': 104.5}
- {'sharpness': 92.4}
- {'kurtosis': 1.9375}
- {'sharpness': 98.4}
- {'var': 0.1628}
- {'kurtosis': 1.632}
- {'sharpness': 111.7}
- {'sharpness': 101.6}
- {'sharpness': 102.2}
- {'entropy': 3.65}
- {'sharpness': 106.56}
- {'kurtosis': 1.801}
- {'sharpness': 111.9}
- {'skew': -2.135, 'kurtosis': 3.42}
- {'sharpness': 103.3}
- {'kurtosis': 1.711}
- {'kurtosis': 3.018}
- {'sharpness': 98.2}
- {'kurtosis': 3.42}
- {'entropy': 3.764}
- {'kurtosis': 1.643}
- {'kurtosis': 2.057}
- {'skew': -1.922, 'kurtosis': 2.652, 'entropy': 3.605, 'darkness': 0.8276}
- {'kurtosis': 1.801}
- {'sharpness': 98.44}
- {'kurtosis': 1.903}
- {'kurtosis': 2.057, 'entropy': 4.16, 'darkness': 0.8354}
- {'entropy': 4.18, 'sharpness': 111.9}
- {'mean': 0.919, 'skew': -3.896, 'kurtosis': 16.1, 'entropy': 3.34, 'darkness': 0.9414}
- {'kurtosis': 2.764}
- {'skew': 2.098, 'kurtosis': 4.418}
- {'skew': 2.078, 'kurtosis': 4.027, 'contrast': 3.365}
- {'kurtosis': 2.025}
- {'mean': 0.884, 'skew': -2.781, 'kurtosis': 7.082, 'entropy': 3.508, 'darkness': 0.933}
- {'kurtosis': 3.006}
- {'entropy': 3.87}
- {'sharpness': 105.1}
- {'kurtosis': 3.516}
- {'sharpness': 93.2}
- {'entropy': 3.76}
- {'kurtosis': 1.623, 'entropy': 3.82, 'darkness': 0.757}
- {'kurtosis': 8.39, 'entropy': 3.889}
- {'kurtosis': 1.964}
- {'kurtosis': 2.22}
- {'kurtosis': 1.71}
- {'kurtosis': 2.346}
- {'entropy': 4.027, 'sharpness': 92.4}
- {'kurtosis': 1.894}
- {'var': 0.1372}
- {'sharpness': 93.56}
- {'entropy': 4.19}
- {'entropy': 3.787}
- {'kurtosis': 3.07}
- {'sharpness': 104.4}
- {'entropy': 4.195}
- {'sharpness': 96.9}
- {'kurtosis': 1.867}
- {'sharpness': 106.6}
- {'sharpness': 105.2}
- {'kurtosis': 3.521}
- {'kurtosis': 1.678}
- {'entropy': 3.883}
- {'kurtosis': 1.611}
- {'sharpness': 106.1}
- {'kurtosis': 2.793}
- {'entropy': 3.973}
- {'kurtosis': 3.123}
- {'var': 0.1405}
- {'entropy': 4.203}
- {'entropy': 4.08}
- {'kurtosis': 2.885, 'darkness': 0.741}
- {'kurtosis': 2.07}
- {'sharpness': 105.94}
- {'entropy': 4.28}
- {'kurtosis': 3.205}
- {'entropy': 3.898}
- {'skew': -3.041, 'kurtosis': 8.945, 'entropy': 3.584}
- {'skew': 2.348, 'kurtosis': 5.652}
- {'kurtosis': 1.621}
- {'kurtosis': 2.078}
- {'sharpness': 102.06}
- {'kurtosis': 1.63}
- {'kurtosis': 1.745}
- {'kurtosis': 6.72, 'entropy': 4.246}
- {'skew': -1.964, 'kurtosis': 3.143, 'darkness': 0.753}
- {'entropy': 3.615}
- {'sharpness': 101.44}
- {'entropy': 4.258}
- {'kurtosis': 2.076}
- {'sharpness': 115.06}
- {'kurtosis': 5.3}
- {'kurtosis': 1.839}
- {'entropy': 3.322}
- {'kurtosis': 2.219}
- {'sharpness': 119.4}
- {'entropy': 3.918, 'sharpness': 99.7}
- {'skew': -2.633, 'kurtosis': 7.086, 'entropy': 4.25, 'darkness': 0.749}
- {'sharpness': 120.2}
- {'entropy': 4.156}
- {'sharpness': 114.3}
- {'kurtosis': 2.082}
- {'sharpness': 109.25}
- {'darkness': 0.745}
- {'sharpness': 94.4}
- {'sharpness': 110.8}
- {'kurtosis': 1.914}
- {'kurtosis': 1.698}
- {'sharpness': 117.6}
- {'darkness': 0.8037}
- {'kurtosis': 1.676}
- {'sharpness': 96.8}
- {'sharpness': 95.7}
- {'entropy': 4.234, 'darkness': 0.7295}
- {'kurtosis': 1.841}
- {'sharpness': 104.94}
- {'sharpness': 96.25}
- {'kurtosis': 1.91}
- {'skew': -2.164, 'kurtosis': 3.588, 'entropy': 3.951, 'darkness': 0.9097}
- {'zeros': 0.0625}
- {'kurtosis': 2.404}
- {'kurtosis': 2.355}
- {'kurtosis': 3.037}
- {'sharpness': 120.2}
- {'mean': 0.898, 'skew': -2.146, 'kurtosis': 3.096, 'entropy': 2.582, 'darkness': 0.9727}
- {'kurtosis': 1.831}
- {'kurtosis': 4.37}
- {'entropy': 3.215}
- {'kurtosis': 2.266}
- {'sharpness': 119.6}
- {'sharpness': 96.06}
- {'kurtosis': 2.092, 'darkness': 0.757}
- {'sharpness': 97.6}
- {'kurtosis': 3.54, 'contrast': 3.303}
- {'skew': -2.186, 'kurtosis': 3.986, 'entropy': 4.098, 'darkness': 0.858}
- {'entropy': 4.008}
- {'entropy': 4.25}
- {'entropy': 3.885}
- {'sharpness': 98.1}
- {'darkness': 0.791}
- {'sharpness': 90.7}
- {'kurtosis': 2.186}
- {'kurtosis': 13.695, 'entropy': 3.572}
- {'kurtosis': 1.856}
- {'kurtosis': 2.658}
- {'kurtosis': 1.681, 'entropy': 3.902}
- {'kurtosis': 1.608}
- {'kurtosis': 1.842}
- {'kurtosis': 1.694}
- {'entropy': 4.004}
- {'kurtosis': 4.695}
- {'sharpness': 92.75}
- {'kurtosis': 3.896}
- {'entropy': 4.273}
- {'sharpness': 97.25}
- {'sharpness': 116.6}
- {'var': 0.1327}
- {'kurtosis': 3.104, 'entropy': 4.266}
- {'kurtosis': 2.344}
- {'sharpness': 103.1}
- {'kurtosis': 1.943}
- {'skew': -2.24, 'kurtosis': 10.555, 'entropy': 4.094}
- {'kurtosis': 2.215}
- {'entropy': 3.596}
- {'sharpness': 105.7}
- {'sharpness': 103.44}
- {'kurtosis': 1.917}
- {'kurtosis': 2.283}
- {'kurtosis': 2.35}
- {'entropy': 4.24}
- {'kurtosis': 1.904}
- {'kurtosis': 2.807}
- {'entropy': 2.775, 'darkness': 0.894}
- {'entropy': 4.277, 'contrast': 3.572}
- {'sharpness': 92.9}
- {'skew': 2.143, 'kurtosis': 3.758, 'entropy': 2.912, 'contrast': 4.523}
- {'sharpness': 110.44}
- {'var': 0.1317}
- {'kurtosis': 1.813}
- {'sharpness': 115.44}
- {'sharpness': 115.1}
- {'entropy': 4.23}
- {'sharpness': 120.2}
- {'entropy': 4.227, 'darkness': 0.749}
- {'entropy': 3.832}
- {'sharpness': 111.4}
- {'var': 0.1307}
- {'skew': -2.154, 'kurtosis': 4.477, 'entropy': 4.22}
- {'sharpness': 105.3}
- {'kurtosis': 1.691}
- {'kurtosis': 2.139}
- {'sharpness': 106.56}
- {'entropy': 3.91}
- {'kurtosis': 1.93}
- {'sharpness': 101.2}
- {'entropy': 3.107, 'zeros': 0.0625}
- {'sharpness': 93.3}
- {'kurtosis': 3.045}
- {'entropy': 3.584, 'darkness': 0.745}
- {'entropy': 4.23}
- {'kurtosis': 4.027}
- {'sharpness': 102.8}
- {'skew': -2.121, 'kurtosis': 4.35, 'entropy': 4.223}
- {'entropy': 3.771, 'contrast': 3.828}
- {'kurtosis': 2.707}
- {'skew': -2.334, 'kurtosis': 5.496, 'entropy': 4.023}
- {'sharpness': 104.0}
- {'entropy': 4.28, 'sharpness': 118.56}
- {'entropy': 4.117}
- {'skew': 2.904, 'kurtosis': 10.59, 'entropy': 4.164}
- {'sharpness': 104.8}
- {'entropy': 4.227}
- {'kurtosis': 3.01}
- {'sharpness': 100.5}
- {'sharpness': 92.0}
- {'var': 0.1378}
- {'var': 0.1305}
- {'entropy': 3.592}
- {'kurtosis': 2.664}
- {'entropy': 3.633}
- {'sharpness': 114.5}
- {'sharpness': 110.2}
- {'entropy': 4.18}
- {'entropy': 3.506}
- {'skew': 2.105, 'kurtosis': 4.902, 'entropy': 4.008, 'contrast': 4.023}
- {'kurtosis': 2.244}
- {'kurtosis': 2.72, 'entropy': 4.19}
- {'kurtosis': 1.699}
- {'kurtosis': 1.615, 'sharpness': 92.1}
- {'entropy': 4.2}
- {'sharpness': 94.3}
- {'kurtosis': 1.762}
- {'entropy': 4.137}
- {'sharpness': 105.2}
- {'skew': -2.002, 'kurtosis': 4.566, 'entropy': 3.275, 'darkness': 0.7715}
- {'sharpness': 105.3}
- {'sharpness': 112.8}
- {'skew': -2.451, 'kurtosis': 6.418}
- {'sharpness': 119.8}
- {'entropy': 3.57}
- {'sharpness': 110.44}
- {'var': 0.1398}
- {'sharpness': 93.5}
- {'entropy': 3.762}
- {'kurtosis': 2.742}
- {'skew': -1.995, 'kurtosis': 3.084, 'darkness': 0.792}
- {'sharpness': 93.2}
- {'sharpness': 99.2}
- {'kurtosis': 3.55}
- {'sharpness': 96.2}
- {'var': 0.1377}
- {'entropy': 4.14}
- {'entropy': 4.06}
- {'skew': 2.879, 'kurtosis': 10.78, 'contrast': 3.383}
- {'kurtosis': 1.93}
- {'kurtosis': 3.256}
- {'sharpness': 117.1}
- {'sharpness': 101.56}
- {'kurtosis': 3.168}
- {'entropy': 4.02, 'contrast': 3.705}
- {'sharpness': 105.25}
- {'kurtosis': 3.133}
- {'sharpness': 114.6}
- {'darkness': 0.753}
- {'entropy': 3.91}
- {'kurtosis': 2.008}
- {'var': 0.1349, 'entropy': 4.242}
- {'sharpness': 98.9}
- {'mean': 0.9883, 'skew': -8.555, 'kurtosis': 77.6, 'entropy': 1.039, 'darkness': 1.0}
- {'var': 0.1556}
- {'skew': -3.092, 'kurtosis': 11.01, 'entropy': 3.867}
- {'sharpness': 109.2}
- {'kurtosis': 1.893}
- {'kurtosis': 2.693}
- {'var': 0.1292}
- {'sharpness': 104.06}
- {'sharpness': 100.94}
- {'skew': -2.115, 'kurtosis': 4.27, 'entropy': 4.07, 'darkness': 0.8237}
- {'kurtosis': 5.41}
- {'sharpness': 95.6}
- {'kurtosis': 1.729}
- {'mean': 0.9175, 'skew': -2.438, 'kurtosis': 6.47, 'entropy': 3.012, 'darkness': 0.894}
- {'kurtosis': 1.996}
- {'kurtosis': 1.947}
- {'kurtosis': 1.896}
- {'kurtosis': 4.547}
- {'sharpness': 105.44}
- {'kurtosis': 2.354, 'entropy': 3.06, 'contrast': 4.45, 'zeros': 0.04166}
- {'kurtosis': 2.307}
- {'sharpness': 98.94}
- {'kurtosis': 2.205}
- {'entropy': 4.0}
- {'skew': -2.188, 'kurtosis': 3.71, 'entropy': 3.664, 'darkness': 0.9136}
- {'sharpness': 103.8}
- {'var': 0.1406}
- {'var': 0.1282}
- {'kurtosis': 5.395}
- {'sharpness': 120.0}
- {'kurtosis': 3.584}
- {'sharpness': 109.9}
- {'var': 0.1628}
- {'kurtosis': 1.703}
- {'contrast': 3.37}
- {'kurtosis': 4.273}
- {'sharpness': 90.6}
- {'skew': 2.605, 'kurtosis': 7.594, 'contrast': 3.652}
- {'sharpness': 101.1}
- {'skew': -2.143, 'kurtosis': 4.82}
- {'skew': 2.377, 'kurtosis': 5.734, 'contrast': 4.086}
- {'sharpness': 99.4}
- {'entropy': 2.8, 'darkness': 0.7686}
- {'kurtosis': 3.791}
- {'entropy': 3.598}
- {'sharpness': 93.0}
- {'entropy': 3.793}
- {'sharpness': 100.44}
- {'kurtosis': 2.021}
- {'sharpness': 103.44}
- {'sharpness': 99.6}
- {'mean': 0.911, 'skew': -2.332, 'kurtosis': 4.33, 'entropy': 2.754, 'darkness': 0.9727}
- {'entropy': 3.877}
- {'entropy': 3.465}
- {'sharpness': 109.44}
- {'sharpness': 105.8}
- {'kurtosis': 2.201}
- {'sharpness': 91.0}
- {'sharpness': 91.25}
- {'kurtosis': 1.705}
- {'sharpness': 97.5}
- {'sharpness': 110.9}
- {'kurtosis': 1.856}
- {'sharpness': 112.44}
- {'sharpness': 114.3}
- {'var': 0.1462}
- {'sharpness': 93.56}
- {'skew': 2.123, 'kurtosis': 8.36, 'entropy': 4.234}
- {'kurtosis': 5.707, 'entropy': 3.76}
- {'kurtosis': 4.96}
- {'kurtosis': 2.02}
- {'sharpness': 101.5}
- {'entropy': 3.877}
- {'sharpness': 98.44}
- {'kurtosis': 3.635}
- {'entropy': 4.24}
- {'sharpness': 91.25}
- {'entropy': 4.28}
- {'sharpness': 110.8}
- {'kurtosis': 2.963}
- {'entropy': 3.283}
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