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
, 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,
will be created in the server running directory.
directory has following folder structure.
datasrc/ ├── 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
set training label data to
The name of image file and corresponded label file name have to be same.
For example, the image file name is
, corresponded label file name
have to be
1.2.1. Format of the data ¶
Format of image files
: ReNomIMG only accepts
formatted image files.
Format of label files
: ReNomIMG only accepts
formatted label files.
The format of xml file is bellow.
<annotation> <size> <width>374</width> <height>500</height> <depth>3</depth> </size> <object> <name>car</name> <bndbox> <xmin>2</xmin> <ymin>3</ymin> <xmax>374</xmax> <ymax>500</ymax> </bndbox> </object> </annotation>
ReNomIMG accepts PASCAL VOC formatted object detection data.
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
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
sampled from the data
that is in the
According to the above figure, you can create
Once a dataset is created its content will never be change.
For creating a
, 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
, ‘’description’’ and
After filling all forms, please push the
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
You can confirm created datasets in the dataset page. For going to the dataset page, please follow the figure below.
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
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