1.1 KiB
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.