During evaluation, the model is tested against the test dataset. This step indicates to the machine vision specialist how well the model will perform in practice.
Users can evaluate and compare their trained networks directly in the tool. The evaluation section provides information on model accuracy, including a heatmap for the predicted classes of all processed images, as well as an interactive confusion matrix to help detect misclassifications. Users can also calculate the estimated inference time per image and export the evaluation results as a single HTML page for documentation purposes.
Currently, evaluation can be performed for the following deep learning methods:
- Classification (video)
- Global Context Anomaly Detection