Trained models can be deployed for use in visual inspections.
Before you begin
Ensure that you have the following GPU storage:
- At least one GPU available.
- Enough GPUs available for your workload. The number of active GPU tasks that you can run at the
same time depends on the number of GPUs on your server.
About this task
For more information about model types, see Model types.
Procedure
-
Open the relevant data set and then click Train model.
-
Select the relevant training type and then click Train model.
Note: When a model is in training, a graph shows the relative performance of the model over time. For more information, see the
Graph for training models.
- Optional:
To stop training a model that is in training or is in the training queue,
click Stop training.
What to do next
If training fails for Detectron2, Faster R-CNN, High resolution, or Anomaly optimized
models, you can slow the training learning rate, and then run the training again. Training for these
models might fail due to the speed of the learning rate. For example, a training configuration that
contains the same model and data set might succeed and fail in two different training tasks that use
the same learning rate