--- title: "Hackathon: YOLO (You Only Look Once) object detection" description: "Can you #replaceadatascientist using an object detection library such as YOLOv4 ? Our two #hackathons proved that libraries can help you a long way, but tweaking and tuning are still needed. So four hours is not enough time to detect a whitefly..." image: https://blogs.itility.nl/hubfs/featured%20image%20yolo%20blog.jpg --- [![itility](https://blogs.itility.nl/hubfs/blog/img/itility%20logo-01.png)](https://www.itility.nl) - Your industry - [Agriculture](https://www.itility.nl/agriculture) - [Energy](https://www.itility.nl/energy) - [Manufacturing](https://itility.nl/manufacturing) - Our expertise - [Business transformations](https://www.itility.nl/business-transformations) - [Software and data](https://www.itility.nl/software-development/) - [IT foundation](https://www.itility.nl/it-foundation) - [Managed services](https://www.itility.nl/managed-services) - [(Generative) AI](https://www.itility.nl/ai/) - [Who we are](https://www.itility.nl/who-we-are) - [Working at Itility](https://careers.itility.nl) - [About](https://itility.nl/about) - [About Itility](https://itility.nl/about) - [Offices](https://itility.nl/about#office-locations) - [Tech partners](https://itility.nl/tech-partners) - [Responsibility](https://itility.nl/responsibility) - [Contact](https://www.itility.nl/#contact) - Your industry - [Agriculture](https://www.itility.nl/agriculture) - [Energy](https://www.itility.nl/energy) - [Manufacturing](https://itility.nl/manufacturing) - Our expertise - [Business transformations](https://www.itility.nl/business-transformations) - [Software and data](https://www.itility.nl/software-development/) - [IT foundation](https://www.itility.nl/it-foundation) - [Managed services](https://www.itility.nl/managed-services) - [(Generative) AI](https://www.itility.nl/ai/) - [Who we are](https://www.itility.nl/who-we-are) - [Working at Itility](https://careers.itility.nl) - [About](https://itility.nl/about) - [About Itility](https://itility.nl/about) - [Offices](https://itility.nl/about#office-locations) - [Tech partners](https://itility.nl/tech-partners) - [Responsibility](https://itility.nl/responsibility) - [Contact](https://www.itility.nl/#contact) [![Picture of Itility](https://blogs.itility.nl/hubfs/Avatar-Itility.png)](https://blogs.itility.nl/en/author/itility?hsLang=en) Written by:   ## [Itility](https://blogs.itility.nl/en/author/itility?hsLang=en) # Hackathon: YOLO (You Only Look Once) object detection **Another fun and educational hackathon at HQ. The goal of this hackathon was to research cutting-edge mobile-optimized object algorithms using our Pest Detection App data set. Note that if you’re not familiar with our Pest Detection App, you can read all about it in [this blog](https://blogs.itility.nl/en/automated-pest-detection?hsLang=en).** **Research phase** The first part of this hackathon consisted of an object detection lecture & demo, which you can watch below, and a model research phase and initial model setup. In the lecture, we discuss the computer vision algorithms we’ve been using and take a deeper look at three components of object detection: data, model architecture, and training loop.   **Implementation** On October 12, we hosted the second part of the hackathon and continued with the object detection implementation based on what we researched in the first hackathon.  Three teams competed to implement the best mobile-optimized fly detection algorithm, by training a model on 2000 labels spread across 34 images and two classes. The models were obtained from the Tensorflow model hub.   The goal was to obtain the best accuracy across three test images. The teams presented their results by pitching their methodology, results, and choices they made.  Some teams managed to predict the images using a pre-trained model but did not manage to train the model on the data. This resulted in their algorithms predicting birds and persons, rather than flies, which were the target classes. The third team did not manage to make any predictions. Turns out that implementing object detection models in a matter of a few hours is not that easy after all. Do you think that you can do better? Download the dataset below if you want to try it yourself. 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