Expensya is a Cloud based multi-platform expense management software. The web and mobile solution covers the entire travel and expense journey: before, during and after each business trip.
One of the main features of Expensya is the automatic extraction of information out of the receipts. Currently, this is done with OCR+. With the emergence of relevant deep learning techniques, Expensya would like to explore the performance of Deep Learning to improve one of the main features of the application, which is the automatic classification of the receipts into different categories, right after snapping a picture of the receipt.
The objective of this challenge is to create a machine learning model to classify images of receipts by the category of the expense.
About Expensya (https://www.expensya.com/en)
Expensya is a French-Tunisian startup that automates expense reports, for 5000 companies in 100 countries, using AI. The solution enables professionals to get rid of this time-consuming task and to manage expense claims effectively. Using powerful AI and Machine Learning algorithms, the Expensya app integrates digitization, archiving, and smart processing technologies, which automatically capture expenses, among other data, in 1 second. Expensya also facilitates the data transmission and integrates perfectly with the accounting software.
The evaluation metric for this challenge is Log Loss.
Your submission file should look like:
img_id cat_1 cat_2 cat_3 cat_4 img_1 1 0 0 0 img_2 0 0 1 0
For example, the above means that:
This is a learning competition. Aside from knowledge, there are no prizes for this competition.
You will receive 25 points for your first submission and 50 points for your first non-sample submission. You can read more about Zindi points here.
As this is a knowledge competition it will not close.
We reserve the right to update the contest timeline if necessary.
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Teams and collaboration
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Datasets and packages
The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.
You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.
If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.
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Submissions and winning
You may make a maximum of 10 submissions per day.
Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.
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