Improve your understanding of qualitative results in semantic segmentation to gain contextual insights into your output data.
What to expect from this page?
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📑 Location of visualizations
Qualitative results for all validation images and ROI(s) are presented in the form of visualizations in separate image files in addition to the PDF report.
📑 Types of visualizations included
ROI(s) overview
If you have included ROI(s) in your validation images, an overview with a visualization of the ROI(s)' position(s) within the image will be provided.
You can find it in the folder rois_visualizations: [your-image-name]_rois_visualization.jpg.
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Please note:
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Image and ROI Performance
You will also find validation visualizations showing the performance of your trained model for a specific label [your-label-name] within the image or the ROI. They are located in the validation_vis folder.
The image and ROI files are named accordingly:
[your-label-name][your-image-name].jpg - visualization name of an image that contains no ROI(s)
[your-label-name][your-image-name]_[roi-id].jpg - visualization name of a ROI
📑 Understanding performance visualizations
Each of the validation visualizations is divided into four sections:
(1) Input image
(2) Prediction visualization
(3) Annotation visualization
(4) Correctness visualization
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Please note: that all visualizations are downscaled to 25 megapixels (MP), if the visualization in the original image size is larger than 25 MP. |
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Input image (Left upper image)
Shows the input image.
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Prediction [your-label-name] (Right upper image)
Shows an overlay of the automated prediction of your trained application on top of the input image.
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Annotation [label-name] (Left lower image)
Shows an overlay of your annotations (ground truth) on top of the input image.
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Correctness (Right lower image)
Visualizes correctness cases resulting from the automated prediction (compared to manual annotations):
True-positive areas are shown in
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Areas where the app's application prediction matches your annotationsannotated and labeled areas. In other words, it predicts and labels image/ROI areas correctly.
False-positive areas are shown in
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Areas that have not been annotated and labeled, yet predicted by your application. In other words, it predicts and labels areas of the image/ROI that should not be predicted and labeled.
False-negative areas are shown in
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Areas that have been annotated , however, and labeled yet were not predicted and labeled by your application.
Now that you have mastered the handling of qualitative results, you are perfectly equipped to gain solid insights from your analysis outputs.
If you still have questions regarding your application training, feel free to send us an email at support@ikosa.ai. Copy-paste your training ID in the subject line of your email.
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