---
# System prepended metadata

title: Detection of Ventilating Bees

---

# Detection of Ventilating Bees

## Group members
* Adriana Pinto (adriana.pinto@fu-berlin.de)
* Lea Muth
* Erik Grahl

## Communication channel

* https://discord.gg/NjrMhTV

## Supervision/Contacts
* David Dormagen
* Benjamin Wild


## Content

### Goal

Honeybees are a popular model system to study collective behavior. Similar to us humans, bees collect and communicate information among their peers. In our project “BeesBook” we try to understand how a colony integrates various kinds of information and “computes” a colony response to the challenges posed by the environment. If the colony gets hot, bees collect water and evaporate it ventilating air. We know that the probability of becoming a “ventilator” drops when a bee had contact with other ventilators. But who starts ventilating first? Your goal in this project is to detect this behavior in image recordings with deep neural networks.

### Literatur

* [Our paper “Automatic localization and decoding of honeybee markers using deep convolutional neural networks”](https://arxiv.org/pdf/1802.04557.pdf)
* [Context paper “Tracking all members of a honey bee colony over their lifetime using learned models of correspondence”](https://www.frontiersin.org/articles/10.3389/frobt.2018.00035/full)
* [Video data](https://box.fu-berlin.de/s/6AXfdgepyD7qbR4)


### Action Plan (To be discussed)

#### Toolchain

* Tensorflow/Pytorch
* Google colab for cloud GPU resources
* Github for version control
* Docker to simplify the enviroment setup
* Github actions for testing and continuous integration


#### Milestones

It is required to deliver three different releases. Each release will be built on top of the foundations of the previous one to finally delivery a working model and its implementation.

##### Release 1

* Environment setup: Repository, python packages and libraries, python foundation
* Roadmap overview by sprints
* Collaboration tools setup (google colab, backlog management - trello, jira)
* Create backlog
* Retrieve and explore data assets

##### Release 2

* Evaluate data quality
* Remove noisy data
* Create training, validation and testing datasets
* Evaluate first network architecture version
* Implement data augmentation, transfer learning if necessary

#### Release 3

* Iterate over model prototypes looking for the best results
* Prepare project documentation 
* Visualize process and results 
* Identify improvements opportunities and next steps
* Deliver final presentation






