The document summarizes how the author collected a dataset of 5,000 images of clothing items to use for a deep learning project. They tried using Amazon Mechanical Turk and Yandex Toloka for crowdsourcing the data collection but had issues with setup and validating data quality. They then collected images themselves and annotated the data. The labeled dataset was uploaded to Kaggle and GitHub for others to access and use in their machine learning projects.

1. BYOD
Bring Your Own Data
Or how I collected 5,000 images of clothes
Alexey Grigorev
22.10.2020

2. Plan
● Why collecting a dataset?
● Collecting a dataset
○ Amazon MTurk
○ Yandex.Toloka
○ Tagias
● Annotating it
● Accessing and using it

3. mlbookcamp.com

4. Which dataset to use for
the deep learning chapter?
🤔

14. 14

15. What if I collected
a dataset myself?
🤔

16. Plan
● Why collecting a dataset?
● Collecting a dataset
○ Amazon MTurk
○ Yandex.Toloka
○ Tagias
● Annotating it
● Accessing and using it

18. Crowdsourcing platforms
● Amazon MTurk
● Yandex.Toloka

19. Amazon MTurk

20. https://blog.mturk.com/tutorial-how-to-create-hits-that-ask-workers-to-upload-files-using-amazon-cognito-and-amazon-s3-38acb1108633
I gave up somewhere here

21. Yandex.Toloka

22. Uploading images of clothes
Take pictures of clothes and upload them
Take as many pictures as possible of the following items:
● T-shirts
● Sweaters
● Shirts
● Jeans and pants
● Dresses and skirts
● ...

23. Important!!!

33. Other options?
Let’s ask the network!

34. https://medium.com/data-science-insider/clothing-dataset-call-for-action-3cad023246c1

38. Plan
● Why collecting a dataset?
● Collecting a dataset
○ Amazon MTurk
○ Yandex.Toloka
○ Tagias
● Annotating it
● Accessing and using it

40. Quality of labels
● Train a simple neural network on all data
● High learning rate, 1-2 epochs
● Apply the model to the dataset and look at the error
● Correct mistakes in labels manually

41. Plan
● Why collecting a dataset?
● Collecting a dataset
○ Amazon MTurk
○ Yandex.Toloka
○ Tagias
● Annotating it
● Accessing and using it

42. https://www.linkedin.com/posts/djemeljanovs_datasciencedj-100daysofdatascience-rigadsclub-activity-6704485793743847425-NsLr/
Uploading to Kaggle
● Run kaggle datasets init
● Update the generated dataset-metadata.json file
● Run kaggle datasets create --dir-mode=tar

43. https://www.kaggle.com/agrigorev/clothing-dataset-full

44. Subset: top-10 classes
● T-shirt (928 items)
● Long Sleeve (576 items)
● Pants (559 items)
● Shirt (345 items)
● Shoes (297 items)
● Dress (288 items)
● Shorts (257 items)
● Outwear (246 items)
● Hat (149 items)
● Skirt (136 items)
https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/
chapter-07-neural-nets/07-neural-nets-train.ipynb
https://github.com/alexeygrigorev/clothing-dataset-small

45. https://medium.com/data-science-insider/clothing-dataset-5b72cd7c3f1f

46. Download
● https://www.kaggle.com/agrigorev/clothing-dataset-full
● https://github.com/alexeygrigorev/clothing-dataset
● https://github.com/alexeygrigorev/clothing-dataset-small

47. Summary
● Amazon MTurk — too much time to set up
● Yandex.Toloka — easy to set up, not easy to validate data
● Networking — great, but difficult and needs good incentive
● Tagias — good quality, 100% certainty data is not copied

48. mlbookcamp.com
● Learn Machine Learning by doing
projects
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49. @Al_Grigoragrigorev
https://airtable.com/shrrHhErcwaqH59TY
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