Darknet GUI (v0.1.4)

Darknet GUI is a tool to train, test and analyze classifier networks by using Darknet framework. It currently works on 64-bit Linux with OpenCV 3.2.

Using Darknet GUI

Clone the repository to your working directory by using the following command:
```
git clone https://github.com/dnzzcn/darknetGUI
```
Or directly download it as a ZIP file and extract it to any directory you want.
Run the program with the command:
```
./darknetGUI
```
If you cannot train the network due to a memory fault, try running the program with root privileges:
```
sudo ./darknetGUI
```

Darknet GUI currently supports the 'Training Tool'. With this tool, you can train a classifier network from scratch with determined hyperparameters, or continue training over a backup weight. You can also analyze the average loss of the network over the training data, and the accuracy of the network over the test data, in real time. The detailed performance analysis of each weight over the test data will be saved under 'accuracy' folder in the Darknet GUI directory, where you can see individual class prediction performances of each tested weight, with which you can build a confusion table.

Dataset Tool: A tool to create a dataset for a Darknet GUI project. You can apply available augmentation methods to your data with specified parameters.
Test Tool: A tool to test previously obtained weights over a specified test data. It also saves the detailed results under the 'accuracy' directory for future analysis.
Analysis Tool: A tool to visualize the existing accuracy results. It will show the overall performance and the class performances of the weights in a graphical interface.
Prediction Tool: A tool that allows you to choose an input image and check the prediction of the network for that image.

Darknet GUI currently supports single GPU training.
Train and test images need to be resized to the input of the network while preparing the dataset. The performance of the network dramatically reduces, otherwise.