Train your own AI Models
With Xailient Console, you can train custom AI models, which you can then deploy to your targeted devices.
To train any AI model, you will need training data.
Xailient Console comes with pre-loaded public datasets.
To train an AI model, from the left menu, under AI Models click Add.
Select dataset(s) by ticking the box(es). You can select a single or combine multiple (both public and private) datasets to train a model.
Enter model name for your AI model.
You can further select only specific subclasses within the (set of) datasets. A single model will train on the combined datasets and predict only those classes you have chosen here.
Select type of AI model
Select a value for Speed vs Accuracy between 1 to 5.
1 is the fastest. It is best for larger objects with simple backgrounds. You should select option 1 if your objects of interest are medium to big (close range).
2 is slower than 1 and should be used if your objects of interest are small to medium (medium range).
3 is slower than 2 and should be used if your objects of interest are small (long range).
4 is slower than 3 and should be used if your objects of interest are smaller and background is complex (longer range).
5 is the slowest. It is best for smaller objects with complex backgrounds. You should use option 5 if your objects of interest are very small and background is complex (longest range).
Select the Cortical Function (Type of AI model you want to train)
Begin training by clicking BEGIN TRAINING.
View Training Status
To view the training status of your AI model, under AI Models click View, and find your model under Training Models. Once the training is completed, you will get an email notification regarding it.
Deploy your AI
When the training is complete, you can build an SDK and install it on the targeted device. For more details on how to build an SDK, refer to Build Deployable SDK section of the documentation
For instructions on how to setup your device, install the SDK and run it, please refer to Deploy You AI section of the documentation.
If your local machines doesn't meet the requirements for native installation we also offer Docker Containers as a deployment option.