1. How to Use ReNomIMG GUI tool

1.1. Start the Application

ReNomIMG is a single page web application. If your installation have done successfully, you can run application in any directory with following commands.

cd workspace # Workspace can be any directory in your pc.
renom_img # This command will starts ReNomIMG GUI server.

For the command renom_img , you can give following arguments.

  • –host : This specifies server address.
  • –port : This specifies port number of the server.

For example, following code runs ReNomIMG with port 8888.

renom_img --port 8888 # Running ReNomIMG with port 8888

If the application server runs, open web browser and type the server address to the address bar like this.


Then the application will be appeared.

1.2. Place your dataset

When the server starts, datasrc directory and storage directory will be created in the server running directory.

The datasrc directory has following folder structure.

  ├── img   # Set training img files here.
  ├── label # Set training label files here.
  └── prediction_set
        ├── img     # Set prediction img files here.
        └── output  # Prediction result will be output here.
              ├── csv
              └── xml

As written in the above comments, please set training image data to datasrc/img , set training label data to datasrc/label .


The name of image file and corresponded label file name have to be same. For example, the image file name is image01.jpg , corresponded label file name have to be image01.xml .

1.2.1. Format of the data

Format of image files : ReNomIMG only accepts JPEG and PNG formatted image files.

Format of label files : ReNomIMG only accepts xml formatted label files. The format of xml file is bellow.


ReNomIMG accepts PASCAL VOC formatted object detection data.

The PASCAL Visual Object Classes

1.3. Create Detection Model

So far, the server and dataset are prepared. Let’s build a object detection model. For building a model, you have to specify dataset and hyper parameters .

1.3.1. Create Dataset

For training a machine learning model, you have to prepare training dataset and validation dataset. Training dataset is used for training model, and validation dataset is used for evaluating a model in terms of how accurately predict data that have not used in training.

In ReNomIMG, training dataset and validation dataset will be randomly sampled from the data that is in the datasrc directory.


According to the above figure, you can create dataset from datasrc. Once a dataset is created its content will never be change.

For creating a dataset , please move to dataset setting modal. Following figures guide you to the dataset page.


Then following page will be appeared.


As you can see, you can specify the dataset name , description and ratio of training data .

After filling all forms, please push the confirm button to confirm the content that the dataset includes.


Then following graph will be appeared. You can confirm what classes are included in the dataset and how many tags are they.

At last, for saving the dataset, please push the save button.

You can confirm created datasets in the dataset page. For going to the dataset page, please follow the figure below.

../_images/how_to_use_gui_dataset_create_button04.png ../_images/how_to_use_gui_dataset_create_button05.png

In the above figure, 2 datasets are already created.

1.3.2. Hyper parameter setting

So far you got all the materials, let’s build a model and run training. For creating a model please push the button Add New Model .


Then you can see a hyper parameter setting modal like following figure.


As you can see in above figure, you can specify following parameters.

  • Dataset Name … Select the dataset for training.
  • CNN architecture … Select the object detection algorithm.
  • Train Whole network … If this is set to True, whole network weight will be trained.
  • Image size … Image size for training.
  • Training loop setting … Number of training and batch size.


Depending on your GPU device, larger image size or batch size causes memory overflow.

1.3.3. Training Model

Finishing hyper parameter settings, then push run button to start training!

If the training starts, model will be appeared in model list and progress bar will be shown.


1.4. Uninstall ReNomIMG

You can uninstall ReNomIMG by following pip command.

pip uninstall renom_img