# Starting project

## Step 1. Create Map
First of all we have to create map for TF Dataset. Using DataSetLoader we have to provide path to images folder and call get_classified_csv(). This will create a Map (Image Path with label). 
This will be later used for creating TF dataset.

## Step 2. Create Dataset
Then you can uncomment lines in main.py for generating dataset. Make sure that you have changed a path. Also change an image size, for this we have parameters like x and y. This way resized images will be saved to dataset. We recommend using x=35 and y=35. That's how you get the best results.

## Step 3. Load datasets to kaggle
Just create a new Dataset and move dataset_YxY after that add to notebook as input

## Step 4. Evaluate in Kaggle
Copy everything from Evaluator_Kaggle.ipynb in Notebook. Don't forget to turn on an GPU Accelarator. That way you will get result a lot faster.

## Optional. Testing locally
You can also train model and start tests locally. For that just start a main.py. Offcourse, don't forget to comment lines for creating a CSV and mapping. Also change a path to wished dataset.