Load Training Dataset
After data preparation, from the Load training data window:
- Click the "..." button to browse & select your training data-set file/folder
DLHUB will use a small data sample to visualize how it looks.
Alphabet categories will be auto-converted to numerical value for training purposes.

If your dataset is images, you can:
- Select Apply Transfer Learning; the input shape array will be automatically populated
- Select Image Augmentation to create more samples if you have a small dataset.
Once the DATA IS GOOD notification appears, click Next to continue.
Configure neural network
To construct the neural network, you can use our neural network template by clicking the Load Template button or start from scratch using Neural Network Modules.

To start constructing a new model, click New.
Add modules
- Select from the left-side panel the module/function of interest.
- Once the module is selected, its parameters will appear in the configuration panel.
- You can modify these parameters to match your application.
- Press ADD to add the module to your neural network. The added module will appear in the neural network listed at the bottom.
- Repeat until you have fully developed your Neural Network.
- You can use the editing tools to shift, duplicate and delete the selected modules in your model.
Save model
After you are done with your configuration, ensure you SAVE the model for reuse next time or share it with others.
Verify model
After saving your model, click Verify Model to start neural network verification
- If it's green (the neural network is valid), click NEXT to continue
- If it's red (the neural network is invalid), you can refer to the status info at the bottom of the screen for verification feedback and make adjustment.

Train AI model
Once the Deep Neural Network is configured, it is ready to be trained to learn features from the dataset.

Training Parameters (1)
The training parameters are required for training algorithms to work and perform well. Specific parameter settings are only available for certain Training Algorithms.
Stopping Criteria (2)
The stopping criteria contain parameters that control when to end/stop the training process.
In short, the Neural Network training is stopped when:
- Training goal is reached.
- Max Fail Count is reached.
Note: You can stop the training anytime and proceed if you think the model's performance is good enough.
Stopping criteria is just settings to stop training automatically.
Start Training (3)
Training will begin immediately once you click Start Training; you may monitor the progress and the Loss and Evaluation values from the graph.
A great feature of DLHUB is that it utilizes GPU for training (if a compatible GPU is detected); this will drastically improve the training speed.
During the training:
- The training dataset is used to adjust the Neural Network weights to optimize the cost/loss function
during the training process. The value of this Loss function is called a performance index
or performance of a training dataset.
- The validation dataset (extracted from the training dataset) prevents over-fitting/over-trained issues.
Once you finish training, click Next to continue
Evaluate the trained model
After a Neural Network has been trained, it will be evaluated to ensure it can cope with a completely new dataset.

Prepare a new dataset with the same format as the training dataset and load it into DLHUB for testing.
Detail about the dataset will be shown in the table for review, and a summary will be displayed for the user.

Once the dataset is loaded, click Evaluate to start the evaluation.

The Accuracy Percentage will be calculated between 0% - 100%, and the quality of the model will also be indicated by the color bar: RED = poor, GREEN = good.
You can retrain the model or click Next to continue to the next step
Test the trained model (optional)
Users can still manually test prediction results before exporting.

- Load your Test dataset by using the browse button (1)
- The dataset will be listed in the Test Files table (2)
- Select each file to test the result
- The result for the selected file will be shown on the right-side panel (3), with the raw calculated score for each class (output)
Export AI model for deployment
Select Export Options and click Export
Note: A typical approach to AI is always trial and error, so training with different datasets
and parameters are recommended to find the best model.