During training, a pretrained classifier is trained on the image dataset that has previously been labeled. With every iteration over the training dataset, the model tries to improve its predictions measured against the validation dataset. Based on its performance, the weights comprising the neural network are adjusted, improving the performance of the next iteration.
In the Deep Learning Tool, users can set all important parameters in the training page. After selecting a data split, the training can be started and the progress and performance are visualized.
Currently, training can be performed for the following deep learning methods:
- Classification (video)
- Global Context Anomaly Detection
- Object Detection
- Instance Segmentation
- Semantic Segmentation