Trained Model Prediction
Learn how to test previously trained models
A previously trained model can be tested/verified by applying the model on a different image set in order to verify how well the model performs.
✓ Step 1: Click on Start prediction button
In the training screen, click on the Start prediction button in order to start the prediction configuration process. A pop-up window will be shown.
✓ Step 2: Select Backend
User can select the backend to run the prediction. The task will be added to the queue and if user selects the cloud backend which has a queued tasks.
✓ Step 2: Select Model Use
This step, the user can choose the type of the model to perform the evaluation on. It can be suitable for Detection, Classification, Semantic Segmentation or Instance Segmentation then click on the next button to move to the next step. For this version, only the instance segmentation type is available.
✓ Step 3: Set Base Model
A model can be chosen among the existing ones.
✓ Step 4: Select Image Set
An image set can be selected for the prediction process.
✓ Step 5: Prediction Summary
At the end of the configuration, the user can visualize a summary of the prediction (Fig.20). If something is not correctly set up, there is the option to click on the previous button to go back to the previous steps and apply changes. Once everything is correctly set, click on the Start Prediction button to start the prediction process.
Prediction may take a while and the user can stop the process by clicking the Stop Prediction button. (Fig.21)
✓ Step 6: Save the predictions as Annotations
Predictions are saved as annotations automatically to use them in future model trainings. User can change the Prediction Threshold value and filter the predictions. (Fig.22)
✓ Step 7: Filter Predictions
When the user reaches the intended value by changing the Prediction Threshold, press Delete Filtered Predictions and delete the rest of them. Remaining predictions will be saved to Annotations automatically. (Fig.23)
✓ Step 8: Predictions as ROI List
The user can check out and control their predictions on the Annotation Screen. They organize them as they are doing annotations. Predictions are separated from the rest of the annotations with the Model Label.
(Fig.24)
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