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]ordataeval[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)
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: 673
# Show each image that has at least one issue
for image, issue in results.issues.items():
print(f"{image} - {issue}")
16 - {'zero': 0.61}
21 - {'zero': 1.33}
57 - {'zero': 6.69}
60 - {'blurriness': 95.75}
62 - {'zero': 0.53}
75 - {'blurriness': 112.38}
81 - {'zero': 3.29}
93 - {'blurriness': 93.06}
102 - {'zero': 1.45}
110 - {'blurriness': 91.88}
111 - {'zero': 0.84}
112 - {'blurriness': 103.75}
118 - {'zero': 5.89}
123 - {'blurriness': 114.44}
162 - {'zero': 1.0}
163 - {'blurriness': 91.44}
176 - {'blurriness': 107.44}
190 - {'blurriness': 90.56}
198 - {'zero': 3.42}
218 - {'brightness': 0.93}
244 - {'blurriness': 111.0}
276 - {'zero': 1.29}
277 - {'blurriness': 93.12}
286 - {'zero': 1.23}
294 - {'zero': 0.69}
322 - {'zero': 0.7}
345 - {'zero': 0.44}
347 - {'blurriness': 103.31}
374 - {'blurriness': 110.56}
385 - {'brightness': 0.91}
387 - {'blurriness': 115.0}
391 - {'blurriness': 107.75}
392 - {'blurriness': 100.12}
414 - {'zero': 0.44}
417 - {'zero': 1.86}
455 - {'blurriness': 92.5}
456 - {'blurriness': 104.12}
535 - {'blurriness': 91.62}
557 - {'zero': 0.82}
564 - {'blurriness': 96.62}
565 - {'zero': 0.58}
605 - {'zero': 5.31}
616 - {'zero': 0.52}
649 - {'blurriness': 94.81}
653 - {'blurriness': 92.0}
671 - {'zero': 1.17}
763 - {'zero': 0.53}
765 - {'blurriness': 113.0}
782 - {'blurriness': 90.56}
802 - {'blurriness': 106.25}
807 - {'blurriness': 110.56}
813 - {'blurriness': 107.25}
834 - {'blurriness': 107.75}
872 - {'zero': 4.89}
888 - {'zero': 3.01}
891 - {'blurriness': 114.12}
911 - {'zero': 0.48}
925 - {'zero': 11.01}
943 - {'blurriness': 109.25}
947 - {'zero': 0.57}
994 - {'zero': 0.99}
1007 - {'zero': 1.23}
1019 - {'blurriness': 100.88}
1039 - {'blurriness': 99.44}
1040 - {'zero': 0.5}
1051 - {'blurriness': 103.19}
1052 - {'zero': 0.98}
1078 - {'brightness': 0.92}
1080 - {'zero': 4.05}
1085 - {'blurriness': 100.69}
1086 - {'blurriness': 92.0}
1108 - {'zero': 1.57}
1150 - {'zero': 0.66}
1156 - {'zero': 3.92}
1164 - {'brightness': 0.88}
1178 - {'blurriness': 95.62}
1240 - {'zero': 0.57}
1249 - {'blurriness': 96.06}
1303 - {'blurriness': 119.81}
1307 - {'blurriness': 103.69, 'zero': 0.89}
1327 - {'zero': 0.82}
1416 - {'zero': 0.56}
1418 - {'blurriness': 104.5}
1441 - {'blurriness': 92.38}
1451 - {'blurriness': 98.38}
1496 - {'blurriness': 111.69, 'zero': 0.94}
1497 - {'zero': 0.48}
1498 - {'blurriness': 101.62}
1510 - {'blurriness': 102.19}
1522 - {'blurriness': 106.56}
1541 - {'blurriness': 111.88}
1561 - {'zero': 0.78}
1579 - {'blurriness': 103.31}
1595 - {'blurriness': 98.19}
1632 - {'zero': 0.74}
1640 - {'zero': 2.96}
1642 - {'blurriness': 98.44}
1651 - {'blurriness': 111.88}
1664 - {'brightness': 0.92}
1671 - {'zero': 0.49}
1689 - {'zero': 0.5}
1694 - {'brightness': 0.88}
1713 - {'zero': 0.51}
1722 - {'zero': 1.69}
1729 - {'zero': 0.53}
1734 - {'zero': 0.57}
1758 - {'blurriness': 105.12}
1762 - {'blurriness': 93.19}
1797 - {'zero': 0.82}
1852 - {'zero': 0.43}
1875 - {'blurriness': 92.38}
1878 - {'zero': 1.0}
1880 - {'zero': 1.51}
1892 - {'zero': 0.5}
1899 - {'blurriness': 93.56}
1918 - {'blurriness': 104.38, 'zero': 0.57}
1921 - {'zero': 2.9}
1928 - {'blurriness': 96.88}
1942 - {'blurriness': 106.62}
1959 - {'zero': 1.83}
1968 - {'blurriness': 105.19, 'zero': 1.1}
1985 - {'blurriness': 106.12}
1988 - {'zero': 0.95}
2010 - {'zero': 9.6}
2067 - {'blurriness': 105.94}
2103 - {'blurriness': 102.06}
2119 - {'zero': 0.82}
2128 - {'blurriness': 101.44}
2139 - {'zero': 0.78}
2166 - {'blurriness': 115.06}
2199 - {'blurriness': 119.38}
2200 - {'blurriness': 99.69}
2201 - {'zero': 0.61}
2214 - {'blurriness': 120.19}
2221 - {'blurriness': 114.31}
2247 - {'blurriness': 109.25}
2258 - {'blurriness': 94.38}
2266 - {'zero': 1.89}
2288 - {'blurriness': 110.81}
2313 - {'blurriness': 117.62}
2318 - {'blurriness': 96.81}
2325 - {'blurriness': 95.69}
2345 - {'blurriness': 104.94, 'zero': 0.77}
2353 - {'blurriness': 96.25}
2381 - {'zero': 4.89}
2390 - {'zero': 1.26}
2394 - {'blurriness': 120.19}
2400 - {'brightness': 0.9}
2405 - {'zero': 0.44}
2435 - {'blurriness': 119.62}
2439 - {'blurriness': 96.06}
2448 - {'blurriness': 97.62}
2484 - {'zero': 0.81}
2486 - {'blurriness': 98.12}
2499 - {'blurriness': 90.69, 'zero': 0.44}
2537 - {'zero': 0.46}
2582 - {'blurriness': 92.75}
2595 - {'blurriness': 97.25}
2608 - {'blurriness': 116.62}
2617 - {'zero': 3.01}
2641 - {'blurriness': 103.12}
2663 - {'zero': 0.89}
2691 - {'blurriness': 105.69}
2699 - {'blurriness': 103.44}
2708 - {'zero': 0.44}
2734 - {'zero': 0.46}
2740 - {'zero': 1.5}
2746 - {'zero': 8.05}
2747 - {'zero': 3.75}
2751 - {'blurriness': 92.88}
2752 - {'zero': 3.76}
2754 - {'zero': 2.93}
2756 - {'blurriness': 110.44}
2757 - {'zero': 0.74}
2763 - {'blurriness': 115.44}
2766 - {'blurriness': 115.12}
2772 - {'blurriness': 120.19, 'zero': 0.95}
2797 - {'blurriness': 111.38}
2807 - {'zero': 0.85}
2824 - {'blurriness': 105.31}
2834 - {'blurriness': 106.56}
2839 - {'blurriness': 101.19}
2840 - {'zero': 15.43}
2841 - {'blurriness': 93.31}
2877 - {'zero': 1.41}
2896 - {'zero': 0.8}
2927 - {'blurriness': 102.81}
2940 - {'zero': 3.79}
2963 - {'zero': 0.58}
2964 - {'blurriness': 104.0}
2967 - {'zero': 1.91}
2968 - {'blurriness': 118.56}
2978 - {'zero': 0.98}
2999 - {'blurriness': 104.81}
3039 - {'blurriness': 100.5}
3056 - {'blurriness': 92.0}
3098 - {'blurriness': 114.5}
3101 - {'zero': 0.92}
3103 - {'blurriness': 110.19}
3119 - {'zero': 1.27}
3125 - {'zero': 2.38}
3165 - {'blurriness': 92.12}
3169 - {'zero': 0.76}
3170 - {'zero': 0.53}
3171 - {'zero': 2.1}
3178 - {'blurriness': 94.31}
3197 - {'blurriness': 105.19}
3201 - {'zero': 1.0}
3209 - {'blurriness': 105.31}
3221 - {'blurriness': 112.81}
3227 - {'blurriness': 119.81}
3252 - {'blurriness': 110.44}
3263 - {'blurriness': 93.5}
3304 - {'blurriness': 93.19}
3310 - {'blurriness': 99.19}
3326 - {'blurriness': 96.19}
3343 - {'zero': 0.81}
3361 - {'zero': 1.19}
3370 - {'zero': 0.5}
3378 - {'blurriness': 117.12}
3379 - {'blurriness': 101.56}
3401 - {'blurriness': 105.25}
3408 - {'blurriness': 114.62}
3422 - {'zero': 0.45}
3443 - {'blurriness': 98.88}
3444 - {'brightness': 0.99}
3472 - {'zero': 0.47}
3480 - {'blurriness': 109.19}
3485 - {'zero': 1.33}
3498 - {'zero': 1.24}
3512 - {'blurriness': 104.06}
3522 - {'blurriness': 100.94}
3541 - {'blurriness': 95.62}
3551 - {'brightness': 0.92}
3561 - {'zero': 0.78}
3576 - {'blurriness': 105.44}
3582 - {'zero': 15.32}
3591 - {'blurriness': 98.94}
3624 - {'blurriness': 103.81}
3629 - {'zero': 0.6}
3633 - {'zero': 0.68}
3650 - {'blurriness': 120.0}
3681 - {'blurriness': 109.88}
3713 - {'zero': 1.81}
3721 - {'blurriness': 90.62}
3725 - {'blurriness': 101.12}
3758 - {'zero': 1.99}
3759 - {'blurriness': 99.38}
3770 - {'zero': 0.53}
3789 - {'blurriness': 93.0}
3797 - {'blurriness': 100.44}
3802 - {'zero': 0.46}
3810 - {'blurriness': 103.44}
3816 - {'blurriness': 99.62}
3823 - {'brightness': 0.91}
3851 - {'blurriness': 109.44}
3863 - {'blurriness': 105.81}
3917 - {'blurriness': 91.0}
3953 - {'blurriness': 91.25}
3963 - {'zero': 0.47}
3976 - {'zero': 0.45}
3977 - {'blurriness': 97.5}
3986 - {'zero': 3.45}
3990 - {'blurriness': 110.88}
4009 - {'zero': 1.15}
4013 - {'zero': 1.85}
4015 - {'zero': 0.53}
4032 - {'zero': 0.97}
4035 - {'zero': 1.62}
4049 - {'blurriness': 112.44}
4052 - {'zero': 0.82}
4059 - {'zero': 1.12}
4060 - {'blurriness': 114.31}
4076 - {'zero': 0.51}
4077 - {'blurriness': 93.56}
4123 - {'blurriness': 101.5}
4132 - {'blurriness': 98.44}
4147 - {'blurriness': 91.25}
4159 - {'blurriness': 110.81}
4204 - {'blurriness': 120.19}
4234 - {'blurriness': 96.75}
4235 - {'blurriness': 91.12}
4272 - {'blurriness': 109.25}
4285 - {'blurriness': 100.12}
4309 - {'blurriness': 108.38}
4325 - {'blurriness': 90.94}
4336 - {'zero': 0.62}
4353 - {'blurriness': 119.88}
4355 - {'zero': 9.65}
4362 - {'blurriness': 94.5}
4364 - {'zero': 0.74}
4373 - {'blurriness': 96.81}
4379 - {'zero': 0.66}
4394 - {'zero': 1.51}
4396 - {'zero': 0.68}
4421 - {'zero': 4.43}
4423 - {'zero': 2.32}
4425 - {'zero': 0.6}
4429 - {'blurriness': 105.06}
4434 - {'brightness': 0.88}
4442 - {'blurriness': 107.75}
4446 - {'blurriness': 107.69}
4448 - {'blurriness': 96.94}
4452 - {'zero': 0.72}
4453 - {'blurriness': 105.44, 'zero': 0.73}
4482 - {'blurriness': 93.69, 'zero': 1.09}
4484 - {'blurriness': 117.44}
4505 - {'blurriness': 119.12}
4513 - {'zero': 1.11}
4534 - {'blurriness': 111.88}
4546 - {'brightness': 0.92}
4552 - {'zero': 0.64}
4564 - {'blurriness': 101.5}
4566 - {'brightness': 0.88}
4567 - {'blurriness': 103.75}
4580 - {'blurriness': 120.19}
4634 - {'zero': 9.69}
4641 - {'blurriness': 99.25}
4668 - {'zero': 2.23}
4669 - {'blurriness': 99.06}
4672 - {'blurriness': 104.06}
4683 - {'blurriness': 100.5}
4702 - {'zero': 0.92}
4711 - {'zero': 0.44}
4753 - {'zero': 3.83}
4756 - {'blurriness': 101.25}
4774 - {'blurriness': 113.62}
4790 - {'zero': 1.17}
4802 - {'zero': 0.77}
4839 - {'blurriness': 92.94}
4847 - {'blurriness': 92.0}
4858 - {'blurriness': 120.19}
4891 - {'zero': 0.48}
4894 - {'blurriness': 119.12}
4902 - {'blurriness': 100.94}
4904 - {'zero': 0.54}
4968 - {'brightness': 0.89}
4985 - {'blurriness': 108.19}
5008 - {'blurriness': 110.69}
5022 - {'blurriness': 119.31}
5023 - {'zero': 1.83}
5026 - {'blurriness': 116.06}
5044 - {'blurriness': 100.56}
5071 - {'zero': 0.7}
5084 - {'zero': 4.76}
5088 - {'zero': 0.7}
5090 - {'zero': 0.71}
5102 - {'blurriness': 100.81}
5139 - {'zero': 1.15}
5148 - {'zero': 0.96}
5171 - {'zero': 1.76}
5172 - {'blurriness': 98.06}
5195 - {'blurriness': 95.0}
5215 - {'zero': 0.59}
5219 - {'blurriness': 117.75}
5244 - {'blurriness': 93.0}
5258 - {'zero': 1.72}
5263 - {'zero': 0.73}
5264 - {'blurriness': 92.44}
5270 - {'zero': 4.31}
5274 - {'blurriness': 93.88}
5284 - {'zero': 7.5}
5289 - {'blurriness': 113.31}
5331 - {'blurriness': 119.0}
5362 - {'blurriness': 92.75}
5391 - {'zero': 0.51}
5402 - {'blurriness': 104.31}
5403 - {'blurriness': 107.88}
5404 - {'blurriness': 111.0}
5423 - {'blurriness': 117.56}
5442 - {'blurriness': 103.19}
5456 - {'zero': 3.81}
5460 - {'zero': 10.62}
5465 - {'blurriness': 91.56}
5473 - {'zero': 0.66}
5478 - {'zero': 2.76}
5495 - {'zero': 1.02}
5496 - {'blurriness': 98.75}
5520 - {'zero': 0.95}
5526 - {'zero': 0.49}
5551 - {'zero': 0.53}
5570 - {'zero': 10.96}
5584 - {'brightness': 0.9}
5591 - {'zero': 3.23}
5595 - {'blurriness': 116.31}
5613 - {'blurriness': 92.06}
5620 - {'blurriness': 120.19}
5642 - {'blurriness': 99.69}
5653 - {'zero': 0.67}
5704 - {'blurriness': 91.31}
5712 - {'blurriness': 92.5}
5723 - {'blurriness': 95.31}
5727 - {'blurriness': 112.25, 'zero': 2.83}
5728 - {'blurriness': 94.69}
5740 - {'blurriness': 120.19}
5743 - {'zero': 3.67}
5774 - {'blurriness': 104.81}
5777 - {'zero': 0.47}
5792 - {'blurriness': 105.88}
5795 - {'blurriness': 96.31}
5799 - {'blurriness': 91.56}
5814 - {'zero': 1.76}
5815 - {'zero': 0.79}
5829 - {'blurriness': 112.0}
5838 - {'zero': 2.67}
5842 - {'zero': 4.22}
5845 - {'blurriness': 102.69}
5856 - {'blurriness': 91.38}
5871 - {'blurriness': 109.62}
5897 - {'blurriness': 98.19}
5908 - {'zero': 1.43}
5922 - {'blurriness': 95.25}
5927 - {'zero': 1.92}
5953 - {'zero': 1.44}
6003 - {'brightness': 0.9}
6015 - {'zero': 0.58}
6026 - {'zero': 3.48}
6035 - {'blurriness': 109.0}
6040 - {'zero': 1.02}
6046 - {'blurriness': 102.81}
6056 - {'blurriness': 91.62}
6065 - {'blurriness': 103.25}
6081 - {'blurriness': 101.31}
6112 - {'zero': 11.85}
6125 - {'blurriness': 91.75}
6127 - {'blurriness': 107.5}
6170 - {'blurriness': 113.75}
6209 - {'zero': 0.49}
6232 - {'zero': 0.78}
6234 - {'zero': 1.58}
6237 - {'blurriness': 111.88}
6265 - {'blurriness': 97.12}
6281 - {'zero': 0.93}
6305 - {'blurriness': 102.56}
6315 - {'zero': 3.25}
6372 - {'zero': 3.63}
6407 - {'blurriness': 101.62}
6450 - {'brightness': 0.9}
6455 - {'zero': 7.61}
6458 - {'blurriness': 105.5}
6494 - {'blurriness': 97.31}
6503 - {'blurriness': 114.62, 'zero': 1.34}
6506 - {'blurriness': 90.81}
6510 - {'zero': 2.94}
6520 - {'blurriness': 119.56}
6538 - {'zero': 0.89}
6551 - {'blurriness': 97.75}
6555 - {'zero': 8.04}
6559 - {'blurriness': 111.12}
6560 - {'blurriness': 100.12}
6564 - {'zero': 0.7}
6567 - {'blurriness': 102.44}
6597 - {'zero': 0.76}
6599 - {'blurriness': 102.88}
6601 - {'blurriness': 95.25}
6636 - {'blurriness': 113.88}
6656 - {'zero': 0.96}
6668 - {'blurriness': 114.88}
6669 - {'blurriness': 112.31}
6694 - {'zero': 8.09}
6703 - {'blurriness': 95.19}
6720 - {'blurriness': 101.94}
6721 - {'zero': 1.11}
6724 - {'zero': 0.67}
6738 - {'blurriness': 95.75}
6740 - {'blurriness': 110.38}
6880 - {'zero': 1.39}
6881 - {'zero': 0.6}
6885 - {'zero': 5.12}
6892 - {'zero': 2.5}
6902 - {'blurriness': 101.12}
6909 - {'blurriness': 92.44}
6920 - {'blurriness': 110.25}
6922 - {'blurriness': 102.31}
6927 - {'zero': 2.53}
6943 - {'blurriness': 110.88}
7015 - {'blurriness': 109.69}
7019 - {'blurriness': 109.0}
7057 - {'blurriness': 98.5}
7092 - {'blurriness': 90.69}
7107 - {'blurriness': 99.75}
7181 - {'blurriness': 93.31}
7190 - {'blurriness': 100.0}
7210 - {'zero': 0.61}
7218 - {'blurriness': 91.94}
7230 - {'blurriness': 104.31}
7241 - {'zero': 5.21}
7245 - {'blurriness': 95.06}
7255 - {'zero': 1.46}
7256 - {'blurriness': 94.12}
7259 - {'blurriness': 91.5}
7264 - {'blurriness': 103.06}
7282 - {'blurriness': 95.31, 'zero': 0.55}
7289 - {'brightness': 0.89}
7301 - {'blurriness': 96.12}
7316 - {'zero': 8.78}
7318 - {'blurriness': 98.44}
7325 - {'blurriness': 114.81}
7336 - {'blurriness': 101.88}
7338 - {'blurriness': 106.44}
7339 - {'blurriness': 112.94}
7359 - {'blurriness': 108.94}
7363 - {'zero': 0.58}
7365 - {'blurriness': 99.19}
7383 - {'blurriness': 108.38}
7384 - {'zero': 2.34}
7391 - {'blurriness': 115.62}
7401 - {'zero': 1.7}
7420 - {'blurriness': 99.56}
7421 - {'zero': 1.24}
7438 - {'zero': 0.61}
7444 - {'blurriness': 104.75}
7449 - {'blurriness': 116.62}
7454 - {'blurriness': 105.56}
7486 - {'blurriness': 107.94, 'zero': 2.1}
7504 - {'zero': 0.56}
7529 - {'blurriness': 113.25}
7537 - {'blurriness': 92.44}
7550 - {'zero': 0.93}
7589 - {'blurriness': 104.31}
7591 - {'blurriness': 96.12}
7606 - {'blurriness': 102.25}
7634 - {'zero': 0.45}
7651 - {'blurriness': 99.38}
7691 - {'zero': 0.46}
7708 - {'blurriness': 120.19}
7779 - {'blurriness': 95.5}
7789 - {'zero': 1.67}
7794 - {'zero': 13.48}
7803 - {'brightness': 0.93}
7811 - {'blurriness': 96.88}
7813 - {'brightness': 0.89}
7821 - {'blurriness': 102.19}
7831 - {'blurriness': 101.56}
7835 - {'zero': 0.84}
7840 - {'zero': 0.78}
7845 - {'zero': 0.85}
7864 - {'zero': 0.99}
7881 - {'blurriness': 105.19}
7900 - {'zero': 1.31}
7907 - {'blurriness': 96.44}
7916 - {'blurriness': 96.56}
8002 - {'zero': 1.77}
8014 - {'blurriness': 94.38}
8017 - {'blurriness': 99.38}
8020 - {'blurriness': 92.5}
8059 - {'zero': 0.53}
8061 - {'brightness': 0.88}
8074 - {'zero': 0.61}
8087 - {'blurriness': 96.06}
8094 - {'zero': 1.53}
8100 - {'blurriness': 102.62}
8109 - {'blurriness': 118.62}
8180 - {'blurriness': 108.81}
8244 - {'zero': 5.01}
8273 - {'blurriness': 91.31}
8309 - {'blurriness': 118.88}
8313 - {'blurriness': 93.81}
8338 - {'blurriness': 99.0}
8344 - {'zero': 0.43}
8359 - {'blurriness': 114.56}
8366 - {'zero': 1.08}
8370 - {'blurriness': 90.56}
8371 - {'zero': 0.51}
8381 - {'zero': 7.43}
8425 - {'blurriness': 110.44}
8435 - {'zero': 2.38}
8448 - {'zero': 3.87}
8469 - {'blurriness': 117.69}
8484 - {'zero': 1.21}
8492 - {'zero': 0.46}
8497 - {'blurriness': 97.75}
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