Analyze any computer vision datasets from Kaggle.

Open in Colab Open in Kaggle

Sign Up

To load any dataset from Kaggle you first need to sign-up for an account. It's free.

On Kaggle, you can browse for a dataset of interest and manually download it on your machine.

Kaggle API

Alternatively, you can use the Kaggle API to programmatically download any dataset using Python.

To install the Kaggle API run

pip install -Uq kaggle

After signing up for an account Kaggle account, head over to the 'Account' tab and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials.

Place this file in the location ~/.kaggle/kaggle.json (on Windows in the location C:\Users\<Windows-username>\.kaggle\kaggle.json. Read more here.

If the setup is done correctly, you should be able to run the Kaggle commands on your terminal. For instance, to list Kaggle datasets that have the term "computer vision", run

kaggle datasets list -s "computer vision"
ref                                                           title                                                size  lastUpdated          downloadCount  voteCount  usabilityRating  
------------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  
jeffheaton/iris-computer-vision                               Iris Computer Vision                                  5MB  2020-11-24 21:23:29           1415         20  0.875            
bhavikardeshna/visual-question-answering-computer-vision-nlp  Visual Question Answering- Computer Vision & NLP    411MB  2022-06-14 04:32:28            421         37  0.8235294        
sanikamal/horses-or-humans-dataset                            Horses Or Humans Dataset                            307MB  2019-04-24 20:09:38           8405        120  0.875            
phylake1337/fire-dataset                                      FIRE Dataset                                        387MB  2020-02-25 16:45:29          12098        180  0.875            
fedesoriano/cifar100                                          CIFAR-100 Python                                    161MB  2020-12-26 08:37:10           4881        116  1.0              
fedesoriano/chinese-mnist-digit-recognizer                    Chinese MNIST in CSV - Digit Recognizer               8MB  2021-06-08 12:15:47            966         45  1.0              
bulentsiyah/opencv-samples-images                             OpenCV samples (Images)                              13MB  2020-05-19 14:36:01           2374         72  0.75             
jeffheaton/traveling-salesman-computer-vision                 Traveling Salesman Computer Vision                    3GB  2022-04-20 01:13:17            183         22  0.875            
sanikamal/rock-paper-scissors-dataset                         Rock Paper Scissors Dataset                         452MB  2019-04-24 19:53:04           4556         78  0.875            
muratkokludataset/dry-bean-dataset                            Dry Bean Dataset                                      5MB  2022-04-02 23:19:30           2303       1464  0.9375           
juniorbueno/opencv-facial-recognition-lbph                    OpenCV - Facial Recognition - LBPH                    6MB  2021-12-01 10:47:12            487         45  0.875            
rickyjli/chinese-fine-art                                     Chinese Fine Art                                    323MB  2020-05-02 03:00:40            821         38  0.8235294        
mpwolke/cusersmarildownloadsmondrianpng                       Computer Vision. C'est  Audacieux, Luxueux,  Chic!  417KB  2022-04-10 21:41:35             10         20  1.0              
paultimothymooney/cvpr-2019-papers                            CVPR 2019 Papers                                      5GB  2019-06-16 18:28:50            934         50  0.875            
emirhanai/human-action-detection-artificial-intelligence      Human Action Detection - Artificial Intelligence    147MB  2022-04-22 21:07:24           1468         40  1.0              
vencerlanz09/plastic-paper-garbage-bag-synthetic-images       Plastic - Paper - Garbage Bag Synthetic Images      451MB  2022-08-26 09:57:18           1127         76  0.875            
shaunthesheep/microsoft-catsvsdogs-dataset                    Cats-vs-Dogs                                        788MB  2020-03-12 05:34:30          27897        345  0.875            
birdy654/cifake-real-and-ai-generated-synthetic-images        CIFAKE: Real and AI-Generated Synthetic Images      105MB  2023-03-28 16:00:29           1702         44  0.875            
ryanholbrook/computer-vision-resources                        Computer Vision Resources                            13MB  2020-07-23 10:40:17           2491         11  0.1764706        
fedesoriano/qmnist-the-extended-mnist-dataset-120k-images     QMNIST - The Extended MNIST Dataset (120k images)    19MB  2021-07-24 15:31:01            844         29  1.0     

See more commands here.

Optionally, you can also browse the Kaggle webpage to see the dataset you're interested to download.

Download Dataset

Let's say we're interested in analyzing the RVL-CDIP Test Dataset.

You can head to the dataset page click on the 'Copy API command' button and paste it into your terminal.

kaggle datasets download -d pdavpoojan/the-rvlcdip-dataset-test

Once done, we should have a the-rvlcdip-dataset-test.zip in the current directory.

Let's unzip the file for further analysis with fastdup in the next section.

unzip -q the-rvlcdip-dataset-test.zip

Once completed, we should have a folder with the name test/ which contains all the images from the dataset.

Install fastdup

Now that we have our dataset in place, let's install fastdup.

pip install fastdup

Now, test the installation by printing the version. If there's no error message, we are ready to go.

import fastdup

Load Annotations



This step is optional. fastdup works with both labeled and unlabeled datasets.

If you decide not to load the annotations you can simply run fastdup with just the following codes.

import fastdup  
fd = fastdup.create(input_dir="IMAGE_FOLDER/")  

Although you can run fasdup without the annotations, specifying the labels lets us do more analysis with fastdup such as inspecting mislabels.

Since the dataset is labeled, let's make use of the labels and feed them into fastdup.

fastdup expects the labels to be formatted into a Pandas DataFrame with the columns filename and label.

Let's loop over the directory recursively search for the filenames and labels, and format them into a DataFrame.

import glob
import os
import pandas as pd

# Define the path
path = "test/"

# Define patterns for tif image found in the dataset
patterns = ['*tif']

# Use glob to get all image filenames for both extensions
filenames = [f for pattern in patterns for f in glob.glob(path + '**/' + pattern, recursive=True)]

# Extract the parent folder name for each filename
label = [os.path.basename(os.path.dirname(filename)) for filename in filenames]

# Convert to a pandas DataFrame and add the title label column
df = pd.DataFrame({
    'filename': filenames,
    'label': label
filename label
0 test/advertisement/12636110.tif advertisement
1 test/advertisement/926916.tif advertisement
2 test/advertisement/502599726+-9726.tif advertisement
3 test/advertisement/509132392+-2392.tif advertisement
4 test/advertisement/12888045.tif advertisement

Run fastdup

To fastdup with the annotations DataFrame, let's point the input_dir to the image folders and annotations to df DataFrame.

fd = fastdup.create(input_dir='test')

Now sit back and relax as fastdup analyzes the dataset.

Broken Images

Let's inspect the dataset to find if we have any broken images.

filename label index error_code is_valid fd_index
18039 test/scientific_publication/2500126531_2500126536.tif scientific_publication 18039 ERROR_CORRUPT_IMAGE False 18039


Let's visualize the duplicates in a gallery.

To get a detailed DataFrame on the duplicates/near-duplicate found, use the similaritymethod.

similarity_df = fd.similarity()  
from to distance filename_from label_from index_x error_code_from is_valid_from fd_index_from filename_to label_to index_y error_code_to is_valid_to fd_index_to
0 5323 17276 1.0 test/resume/0001489550.tif resume 5323 VALID True 5323 test/memo/0001461863.tif memo 17276 VALID True 17276
1 21188 2189 1.0 test/scientific_report/2056457981.tif scientific_report 21188 VALID True 21188 test/advertisement/91572245_91572246.tif advertisement 2189 VALID True 2189
2 2358 1353 1.0 test/advertisement/2072281170.tif advertisement 2358 VALID True 2358 test/advertisement/2073352207.tif advertisement 1353 VALID True 1353
3 26877 1353 1.0 test/news_article/2083785419.tif news_article 26877 VALID True 26877 test/advertisement/2073352207.tif advertisement 1353 VALID True 1353
4 20715 1353 1.0 test/scientific_report/2505149213.tif scientific_report 20715 VALID True 20715 test/advertisement/2073352207.tif advertisement 1353 VALID True 1353

We can get the number of duplicates/near-duplicates by filtering them on the distance score. A distance of 1.0 is an exact copy, and vice versa.

near_duplicates = similarity_df[similarity_df["distance"] >= 0.99]
near_duplicates = near_duplicates[["distance","filename_from", "filename_to", "label_from", "label_to"]]

Slice the DataFrame to view related columns.

distance filename_from filename_to label_from label_to
1.0 test/resume/0001489550.tif test/memo/0001461863.tif resume memo
1.0 test/scientific_report/2056457981.tif test/advertisement/91572245_91572246.tif scientific_report advertisement
1.0 test/advertisement/2072281170.tif test/advertisement/2073352207.tif advertisement advertisement
1.0 test/news_article/2083785419.tif test/advertisement/2073352207.tif news_article advertisement
1.0 test/scientific_report/2505149213.tif test/advertisement/2073352207.tif scientific_report advertisement



That's a lot of (1392) duplicates! Not cool for a test dataset. Using fastdup we just conveniently surfaced these duplicates for further action.

Typically, we'd just remove these duplicates from the dataset as they do not add value. But we will leave this step to you as the data curator.

Image Clusters

fastdup also includes a gallery to view image clusters.




The components gallery gives a bird's eye view of how similar images exists in your dataset as clusters.

Statistical Gallery

View the dataset from a statistical point of view to show bright/dark/blurry images from the dataset.




Not all bright/dark blurry images are useful. In this dataset, we found documents that are totally black or white. We'll leave it to you to decide whether these images are useful.

View DataFrame with image statistics.

index img_w img_h unique blur mean min max stdv file_size contrast filename label error_code is_valid fd_index
0 762 1000 255 21070.7559 229.3038 0.0 255.0 72.2897 106922 1.0 test/advertisement/12636110.tif advertisement VALID True 0
1 762 1000 256 12831.0820 229.3935 0.0 255.0 74.0765 62048 1.0 test/advertisement/926916.tif advertisement VALID True 1
2 754 1000 256 41271.8359 196.6943 0.0 255.0 69.2706 589350 1.0 test/advertisement/502599726+-9726.tif advertisement VALID True 2
3 754 1000 256 15565.9248 243.6986 0.0 255.0 46.8760 73854 1.0 test/advertisement/509132392+-2392.tif advertisement VALID True 3
4 762 1000 256 11803.9893 247.4764 0.0 255.0 39.0911 54344 1.0 test/advertisement/12888045.tif advertisement VALID True 4


Since we ran fastdup with labels, we can inspect for potential mislabels. Let's first visualize it via the similarity gallery.




In the similarity gallery fastdup surfaces the images that are visually similar to one another yet has different labels.

Wrap Up

That's it! We've just conveniently surfaced many issues with this dataset by running fastdup. By taking care of dataset quality issues, we hope this will help you train better models.

Questions about this tutorial? Reach out to us on our Slack channel!

VL Profiler - A faster and easier way to diagnose and visualize dataset issues

The team behind fastdup also recently launched VL Profiler, a no-code cloud-based platform that lets you leverage fastdup in the browser.

VL Profiler lets you find:

  • Duplicates/near-duplicates.
  • Outliers.
  • Mislabels.
  • Non-useful images.

Here's a highlight of the issues found in the RVL-CDIP test dataset on the VL Profiler.


Free Usage

Use VL Profiler for free to analyze issues on your dataset with up to 1,000,000 images.

Get started for free.

Not convinced yet?

Interact with a collection of dataset like ImageNet-21K, COCO, and DeepFashion here.

No sign-ups needed.