2. How to use the ReNomIMG GUI

2.1. Start the Application

ReNomIMG is a single page web application. After installation is finished, you can run the application in any directory with the following commands.

cd workspace # The workspace can be any directory in your PC.
renom_img # This command starts the ReNomIMG GUI server.

The command renom_img accepts the following arguments.

  • --host : This specifies the server address.
  • --port : This specifies the server port number.

For example, the following command runs ReNomIMG on port 8888.

renom_img --port 8888 # Run ReNomIMG on port 8888

After starting the application server, open your web browser and type the server address into the address bar.


The ReNomIMG GUI will load in your web browser.

2.2. Provide Image and Label Data

When the server starts, it will create datasrc and storage directories in the current running directory if they don’t already exist.

The datasrc directory has the following folder structure.

  ├── img   # Place training img files here.
  ├── label
  │   ├── classification # Place classification training label files here
  │   ├── detection      # Place detection training label files here
  │   └── segmentation   # Place segmentation training label files here
  └── prediction_set
      └── img   # Place prediction img files here.

As shown in the structure above, please place the training image data in datasrc/img , and the training label data in datasrc/label .


The name of the image file and corresponding label file name must be the same. For example, for object detection data if an image file name is image01.jpg , the corresponding label file name must be image01.xml .

2.2.1. Format of Detection data

Object detection image and label files should conform to the formats below.

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 the xml file is shown below.

Place xml files here : <ReNomIMG dir>/datasrc/label/detection/<sample.xml>


ReNomIMG accepts the PASCAL VOC format for object detection data.

The PASCAL Visual Object Classes

2.2.2. Format of Classification data

Classification image and label files should conform to the formats below.

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

Format of label files : ReNomIMG only accepts txt formatted label files. The format of the text file is shown below.

Please save the file as target.txt .

Place label file here : <ReNomIMG dir>/datasrc/label/classification/target.txt

crayfish_image_0035.jpg crayfish
crayfish_image_0065.jpg crayfish
crayfish_image_0037.jpg crayfish
crayfish_image_0032.jpg crayfish
crayfish_image_0028.jpg crayfish
crayfish_image_0051.jpg crayfish
wrench_image_0035.jpg wrench
wrench_image_0037.jpg wrench
wrench_image_0032.jpg wrench
wrench_image_0028.jpg wrench
wrench_image_0019.jpg wrench
wrench_image_0031.jpg wrench
pigeon_image_0035.jpg pigeon
pigeon_image_0037.jpg pigeon
pigeon_image_0032.jpg pigeon
pigeon_image_0028.jpg pigeon
pigeon_image_0019.jpg pigeon
pigeon_image_0031.jpg pigeon
pigeon_image_0012.jpg pigeon
pigeon_image_0002.jpg pigeon
pigeon_image_0015.jpg pigeon
pigeon_image_0042.jpg pigeon
pigeon_image_0036.jpg pigeon
pigeon_image_0022.jpg pigeon
pigeon_image_0021.jpg pigeon
pigeon_image_0029.jpg pigeon

ReNomIMG accepts the PASCAL VOC format for classification data.

The PASCAL Visual Object Classes

Class numbers are assigned to each class based on the alphabetical order of the class names, beginning with 0.

2.2.3. Format of Segmentation data

Semantic segmentation image and label files should conform to the formats below.

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


Segmentation requires two kinds of label data. PNG files (one per image) and class_map.txt (one per dataset).

Format of label files : ReNomIMG only accepts txt formatted files for class labels and PNG files for image label data. The format of the txt file is shown below.

Please save the class label list as class_map.txt .

Place class label file here : <ReNomIMG dir>/datasrc/label/segmentation/class_map.txt

Example of good data

  • Class id number starts at 0, which is set to the background class.
  • Class id numbers are serially numbered.
background 0
airplane 1
bicycle 2
bird 3
boat 4
bottle 5
bus 6
car 7
cat 8
chair 8
cow 10
diningtable 11
dog 12
horse 13
motorbike 14
person 15
potted plant 16
sheep 17
sofa 18
train 19
tv/monitor 20

Example of incorrect data

  • Class id number does not start at 0.
  • Class names do not include a background class.
  • Class numbers are not serially numbered.
airplane 1
bicycle 10
bird 50
boat 100
bottle 150
bus 200
car 250
cat 300
chair 350
cow 400
diningtable 450
dog 500
horse 550
motorbike 600
person 700
potted plant 750
sheep 800
sofa 900
train 950
tv/monitor 1000

The following is a sample segmentation label PNG file. The class id numbers have been mapped to colors with a color map for visualization purposes.


ReNomIMG accepts the PASCAL VOC format for semantic segmentation data.

The PASCAL Visual Object Classes


The name of the image file and corresponding label file name must be the same. For example, if the image file name is image01.jpg , the corresponding label file name must be image01.png .

2.3. Create Model

The application server and dataset are now both ready, so let’s build an object detection model. To build a model, you must specify the dataset and the training hyper-parameters.

2.3.1. Create Dataset

To train a machine learning model, you should prepare training and validation sub-datasets. The training sub-dataset is used for training the model, and the validation sub-dataset is used for evaluating how accurately the model can predict data that has not been used in training.

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


As shown in the figure above, you can create a dataset from the datasrc images. Once a dataset is created its contents will never change.

For creating a dataset, please open the dataset modal. The following figures guide you through this step.


The following page is displayed next.


As shown above, you can specify the dataset name, description and ratio of training to validation data.

After entering this information, click the Confirm button to generate the sub-datasets.

The following visual will be shown. You can confirm what classes exist in the dataset, their ratios, and the total number of images.


Finally, to save the dataset click the Submit button.

You can confirm all datasets you have created on the dataset page. To access the dataset page, please follow the steps shown below.

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

In the figure above, 2 datasets have already been created.

2.3.2. Configure Hyper-parameters

After completing the steps above, you can build a model and start training it. To create a model, click the button +New button.


This will open a hyper-parameter configuration modal, as shown below.


As seen in the figure, you can specify the following parameters.

  • Dataset Name … Select the dataset for training.
  • Algorithm … Select the CNN algorithm.
  • Batch Size … Set the batch size. A larger number can speed up training but requires more memory.
  • Total Epoch … Set the number of times your model should pass through the dataset during training. All images are seen once in every epoch.
  • Image Width … Image width for resizing images during training.
  • Image Height … Image height for resizing images during training.
  • Load pretrained weights … Check this box to load the pretrained weights for the algorithm as initial weight values. If unchecked, the weights are randomly initialized.
  • Train whole network … Check this box to train all layers of the model. If unchecked, the pretrained layers will be frozen during training.


Depending on your GPU device, a larger image size or batch size may cause a memory overflow.

2.3.3. Train the Model

After configuring the hyper-parameters, click the Create button to start training!

As training begins, the model will be added to the model list and the train progress bar will also appear.



The same procedure for building and training a model can be used for Detection, Segmentation and Classification.

2.3.4. Understanding model performance

As training proceeds, the Learning Curve window will be updated with the average loss values for the training and validation datasets for each epoch. The values may fluctuate, but in general a properly training model will display decreasing loss values as a function of epoch number. The Best Epoch Line on the plot corresponds to the epoch with the best evaluation metric on the validation dataset. This is determined by the mAP score for Detection models and the F1 score for Classification and Segmentation models.


2.4. Perform Predictions

After training is finished, we can use the model for making predictions with new image data.

Click the Deploy button shown in the Model Detail window on the Train Page to select which model to use for performing predictions on new data. The currently deployed model is shown at the top of the model list with a status of ‘Deployed’.


After deployment, open the Predict page using the side bar menu. The following figure shows the Predict page.


To run the prediction using the deployed model, click the Run Prediction button.


The images used for predictions are all those contained in the datasrc/prediction_set/img directory. The directory structure is described in Provide your dataset .

After the predictions are made, the results are displayed in the window.


You can also download the prediction results as a csv file. Click the Download button on the top right to download the file.


2.5. Uninstall ReNomIMG

You can uninstall ReNomIMG with the following pip command.

pip uninstall renom_img