From 2d30ab7e35fef67121895c9d57e5234fa6070f68 Mon Sep 17 00:00:00 2001 From: Kamila Bobkowska Date: Sun, 7 Jun 2020 14:52:58 +0000 Subject: [PATCH] updating for 1st project explanation --- final-evaluation.md | 1 + 1 file changed, 1 insertion(+) diff --git a/final-evaluation.md b/final-evaluation.md index e2067b0..fc8a4b8 100644 --- a/final-evaluation.md +++ b/final-evaluation.md @@ -49,6 +49,7 @@ Since there are three people in our group we have created and combined three sub ### First subproject The first subproject was created by Patryk Krawiec (the report can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Patryk_Krawiec.md)). +This project used **Deep Neural Network** to recognize house numbers visited by the Garbage Truck agent. It is implemented from scratch, has 4 layers and achieved about 97% accuracy. Subproject is mostly located in `numbering.py` file and affects our main project in `Main.py`. To use it properly the concept of paying and not-paying customers was introduced. At the beginning dumpsters are divided between those 2 categories and trash is taken out only when the first one is visited. Dataset on which the network was taught is sklearn dataset of digits. ### Second subproject The second subproject was created by Kamila Bobkowska (the report can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Kamila_Bobkowska.md)). This project used **Convolutional Neural Networks** to detect what kind of trash is in the dumpster and sorts it accordingly. It was mainly implemented to to the class `Garbagetruck.py` and `dumpster.py` . It used photos of garbage to determine whether it was sorted correctly. It did so by using CNNs - extracting the most important features from the photos with convolution layers, then using pooling layers and flattening. Along with implementing CNNs the person created a function that assigns trash to dumpsters (between 1 to 3 trash correctly sorted and 0 to 2 incorrectly sorted trash). The data set used was from [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).