Commit 41ad8ef7 authored by Herman Andersen Dyrkorn's avatar Herman Andersen Dyrkorn
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Update readme.md

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...@@ -108,7 +108,7 @@ When a user uploads an image it will be temporarily be stored in a folder called ...@@ -108,7 +108,7 @@ When a user uploads an image it will be temporarily be stored in a folder called
Use DeeplabCut's GUI tool to label the images. We recommend using the anaconda environments provided by DeepLabCut as they contain all necessary dependencies. An explanation of how to label the images is shown below. If you wish to use the previously labeled images your DeepLabCut has to be created with Project name ```Salamander-Abdomen``` and experimenter name ```Experiment```. This is important as the files with your labels/annotations will be named depending on these factors. After all images have been labeled a training set can be generated. When training a model you can continue using the GUI version of DeepLabCut as long as you have an Nvidia GPU with Cuda 10 or lower compatibility. If you have an Nvidia GPU which requires Cuda 11 you will have to use DeepLabCut-core as it supports Cuda 11 and Tensorflow 2.x. We used DeepLabCut-core both for training and for estimating images inside our working application. The command ```pip install git+https://github.com/DeepLabCut/DeepLabCut-core``` will install DeepLabCut-core. As this library gets updated frequently we recommend looking at the newest documentation for installation and usage. Use DeeplabCut's GUI tool to label the images. We recommend using the anaconda environments provided by DeepLabCut as they contain all necessary dependencies. An explanation of how to label the images is shown below. If you wish to use the previously labeled images your DeepLabCut has to be created with Project name ```Salamander-Abdomen``` and experimenter name ```Experiment```. This is important as the files with your labels/annotations will be named depending on these factors. After all images have been labeled a training set can be generated. When training a model you can continue using the GUI version of DeepLabCut as long as you have an Nvidia GPU with Cuda 10 or lower compatibility. If you have an Nvidia GPU which requires Cuda 11 you will have to use DeepLabCut-core as it supports Cuda 11 and Tensorflow 2.x. We used DeepLabCut-core both for training and for estimating images inside our working application. The command ```pip install git+https://github.com/DeepLabCut/DeepLabCut-core``` will install DeepLabCut-core. As this library gets updated frequently we recommend looking at the newest documentation for installation and usage.
<p align="center"> <p align="center">
<img src="documentation/lable.png" alt="salamander" width="300"/> <img src="documentation/lable.png" alt="salamander" width="800"/>
</p> </p>
In the above image, a labeled salamander is shown. It is important to define the same body parts as in our project. Inside the ```config.yaml``` file in our project the body part definitions are found. Feel free to copy these to your own project. Bodyparts 1 through 4 represent the spine of the salamander and begin at the chest down to the pelvis. Make sure to label the chest first, and move down to the pelvis! The last two points represent the shoulders where the left and right points represent the salamander's left and right shoulder. In the above image, a labeled salamander is shown. It is important to define the same body parts as in our project. Inside the ```config.yaml``` file in our project the body part definitions are found. Feel free to copy these to your own project. Bodyparts 1 through 4 represent the spine of the salamander and begin at the chest down to the pelvis. Make sure to label the chest first, and move down to the pelvis! The last two points represent the shoulders where the left and right points represent the salamander's left and right shoulder.
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