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Quality Automation with AI and Relimetrics

Training Tutorial

Train AI models with Reli-Vision

The training process consists of the following steps:

✓ Step 1:  Select Training Tab 

From the side context menu, select the Training tab in order to start the training configuration process. Click ‘Start Training’ button on the top right corner. 

✓ Step 2:  Select a Roadmap 

With this step, the user will be doing settings for the training. Firstly, the user can choose a roadmap. The following steps are changing slightly whether it is first training or retraining. For this use case first training  is selected.

Training Screen - Selecting a Roadmap

✓ Step 3:  Select a Training Server 

In this step, the user can choose the training server to perform the training on. Once selected, click on the Connect button. The task will be added to the queue if there is a queue in the selected backend. When the queue is done, the task will be performed automatically. For this use case, a local backend is selected. 

Training Screen - Selecting Backend

✓ Step 4:  Select Model Use

During this step, the user can choose the type of the model to perform the training on. It can be suitable for Detection, Classification, Semantic Segmentation or Instance Segmentation then click on the next button to move to the next step. For this use case detection is selected.

Training Screen – Selecting Training Type

✓ Step 5:  Select Image Set

An image set can be chosen among the existing ones. Image sets should be splitted to proceed. If there is an image that has not splitted before, user can click on it and split it automatically. 

                Training Screen – Selecting Image Set

✓ Step 6:  Select Component 
Components can be selected among the existing ones. Once decided, click on Next.
✓ Step 7:  Set Base Parameters
Detecting the optimal parameters for the training may take a while. After the detection is completed, the user can see the optimal settings for the training. The user can also change the settings under ‘Advanced Options’. 

 Training Screen – Setting Base Parameters

✓ Step 8:  Select Architecture
In this step, two options are available for the user as Basic and Advanced. In the Basic tab, recommended range is given for the training. User can define the maximum time. With this information, optimal parameters will be calculated and applied for the training. Please keep in mind that roughly, longer time brings more accurate results. 
 Training Screen – Selecting Architecture - Basic

In the Advanced tab, estimated inference time is already calculated for given architectures. There are several use cases for this step.
- User can select the architecture and click Next if they exactly know which architecture to use without diagnosing.
- The user can select one of the architectures, click Next and start diagnosing for the selected one.
- The user can diagnose all the architectures. This process may take longer.

Diagnosing is recommended for this step. Diagnosing proves that your local resources are sufficient and calculates  the training time. 

Training Screen – Selecting Architecture - Advanced

✓ Step 9:  Set Training Parameters 

Detecting the optimal parameters for the training may take a while. When the detection is done the optimal parameters are set. This part includes hyperparameters and augmentation parameters. The user can also change the settings under ‘Advanced Options’. 

Training Screen – Setting Training Parameters

✓ Step 10:  Set Model Name

A new model name should be defined in this step.User can use the recommended name or create a new name for the model.  Once entered, click on Next.

Training Screen – Setting Model Name

✓ Step 11:  Training Summary

At the end of the training configuration, it is possible to visualize the summary of the training. If something is not correctly set up, there is the option to click on the previous button to go back to the previous steps and apply the required changes. Once everything is correctly set, click on the Start training button to start the training process.

✓ Step 12:  Start Training 

Click on the Start Training button in order to start the training configuration process. The Training process will be shown.

While the training progresses, the user can see model loss and IoU (Intersection over Union) plots in real time. These plots are used to determine how well the training is proceeding. In a successful training session, the model loss graph is expected to converge to 0 and model accuracy to 1. If these graphs do not converge and show irregular behavior, this indicates that the training is not proceeding as expected. Possible reasons for an unsuccessful training session are user mistakes during annotations and/or not having enough images to train the model. At any time, a training session can be stopped and annotations can be reviewed in the gallery screen.

Training Screen - Training Graphs

✓ Step 13:  Start Evaluation 

Once the training is complete, a pop-up window will be displayed stating that the Training is finished. After Clicking on the Check Details button, the view switches to the Evaluation Tab, which shows training statistics, including the total number of training images used, total loss, false positives, calculation time and the number of classes used. 

Figure 17 - Training Screen - Evaluation Tab

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