Robovision AI 5.11 release notes¶

Robovision AI 5.11.1
Robovision AI 5.11

Robovision AI 5.11.1¶

Release date: October 31, 2025

What's new¶

Improved AI-ADC workflow

When a prediction’s confidence during model testing falls below the threshold, the system now automatically adds the metadata Unknown: true—without overwriting the original predicted class.

You can easily identify or filter these low-confidence predictions by creating a custom metadata filter with the condition Unknown is true. Data marked as unknown can also be captured during inference.

Known limitations¶

General

The Robovision AI UI has been optimized for the browser size of 1920x1200.
It is not possible for users to duplicate a tab if the environment is using the HTTP protocol. This does not affect environments using HTTPS. As a workaround, do one of the following:
- Open multiple browser windows instead of duplicating tabs.
- In Chrome, configure the HTTP URL to be treated as secure via chrome://flags.
If the storage is full, some functions may become unavailable, for example, saving annotation or training a model.
Upgrades are supported up to two minor versions forward from your current version.
When upgrading to a new version, new algorithm versions are installed, while preserving the existing ones. Existing projects will be updated to utilize the new algorithms.
If the terms and conditions change, you will not be prompted to read and accept them again.
Notifications may be missing in specific cases, such as:
- When copying of annotations fails because the storage is almost full.
- When an inference setup import fails due to nearly full storage.
When recovering from an invalid or expired license, a 404 page may appear. To resolve the issue: Log out and log back in to refresh your licensing information.

Browser support

The Robovision AI platform has been designed and validated for Google Chrome 117 or later.
You can transfer data between Robovision AI platforms only in Chromium-based browsers (Google Chrome, Microsoft Edge, and more).
In Google Chrome, training metrics and a final validation score might not be displayed if an ad blocker is used. To prevent this, mark the Robovision AI URL as trusted in the Google Chrome settings.
In Google Chrome, the thumbnail view can load slowly if you have password managers like Lastpass activated. To prevent this, disable the extension or work in a guest profile window.

Backward compatibility

Backward compatibility with Robovision AI versions 3.x and 4.x is not foreseen. But upon request, Robovision will provide the needed assistance and conversion scripts.

Branding and brand assets

Upon upgrading to a newer version, Robovision AI may include new UI text that has not been customized. To ensure alignment, export the template containing the UI text and review for any necessary updates (see step 3 in Change brand assets).

Data recording

Samples recorded while the recording session is stopping are missing from the recording session logs.

Data upload

In general and with respect to the model testing functionality, the following image types are supported: JPG, JPEG, PNG, 8-bit TIF and TIFF, BMP, GIF, including a 4^th transparency channel. EXIF orientation metadata is supported.
File names must contain only Latin (ASCII) characters. There are known rendering, packing, and backup/restore issues with file names/object keys that contain non-Latin characters.
You will be logged out of the platform if your data import has been in the Uploading stage for more than an hour.
Limited functionality projects: You can still import data if the label center is empty.

Data center

When the number of imports exceeds 2,500, the imports page in the data center may experience slow loading times. Removing unnecessary imports can help improve performance.

Labeling

2D labeler performance (in particular the Magnetic lasso and Grab cut tools) may degrade for high-resolution images.
You may face the following issues when using the Prediction tool in the label center:
- The prediction process gets hidden if you leave the label center. To view it again, click the Prediction tool.
- The predicted labels are not added to the samples as the prediction process is running.
- The Status section is not updated as the prediction process is running.
If you select more than 100 samples in the label center, the Selected annotations panel on the right includes no information about individual samples. As a result, you will not be able to copy or delete individual annotations.
When copying or replacing annotations for multiple samples, the same source should be selected for each sample (for example, annotations from one particular member or one prediction job).
EfficientNet: When you re-annotate already annotated samples in bulk, you will not be notified that existing annotations will be overwritten.

Pagination

In all views except the single view, a maximum of 100 samples is displayed per page. If you select samples and then go to another page, these samples will no longer be selected.
In the test center, you can't select samples beyond the current page (more than 100 samples). The same applies when you view samples in a dataset.

Classes

In the label center, it is possible to create a class with a name that is just a space.
The class name is not saved if you collapse the Classes section without pressing Enter.

Charts

Class distribution:
- Label and test centers: If you apply a filter when the chart is already open, the filter isn't considered. To refresh the chart, click Clear in the upper-right corner of the Chart setup panel, and then set up the chart again.
Confusion matrix:
- In both the UI and exported CSV files, percentages are rounded to whole numbers.
- Label center:
  - If you apply a filter when the chart is already open, the filter isn't considered. To refresh the chart, click Clear in the upper-right corner of the Chart setup panel, and then set up the chart again.
  - When you generate the chart for filtered data, some information in the chart might be inaccurate.
- Test center:
  - YOLOv8+: For some samples, no model annotations are displayed in the chart, even though the annotations exist.
  - The chart doesn't consider the filters you have applied—the number and percentage of samples labeled with a certain class is incorrect. To view the actual number of samples, you'll need to click the needed cell.
Wafer map:
- The wafer map chart is optimized for the browser size of 1920x1080 in full-screen mode. To enter full-screen mode, press F11.
- When the wafer map contains more than 100,000 samples, selection and filtering within the chart may affect performance and increase the loading time.

Model training

PIDNet: When labeling a consistent area with the ignore class, the model might predict every class in those areas. To avoid this, ensure there is some variation in the areas labeled as ignore.
PIDNet: If the entered batch size is too large, no validation message is displayed, and the training cannot be started.
YOLOv8+: Subtle differences in score calculations during model validation and in the test center may result in discrepancies between the scores, even when using the same settings and data.
During training setup, the Add model button is enabled even if there are no models available for transfer learning.

Model testing

If you test a model on tagged data without comparison, the test data in the test center may disappear after you unassign that tag. However, the training center will still display the correct number of samples used in the test.
Filtering is based on predicted classes from the first model only—not ground truth or second model predictions.
Tests may fail if:
- The test name contains exactly 255 characters.
- The test dataset has been deleted.
- You set the confidence threshold to 1 for both models when testing on tagged or imported data. The system requires at least one predicted example to compute metrics.
When a user tests a model on a dataset from which samples were deleted, the test runs without errors and evaluation metrics are correctly calculated and displayed on the project details page and in the training center. However, the test results are not displayed in the test center.
When you export test results, the name of the CSV file contains the project and test names from the time the test was run.

Cameras and camera configurations

An edited camera configuration needs to be re-applied in an inference setup. To use the updated camera configuration, edit your inference and re-apply the configuration.
A camera that has been replaced in a camera configuration cannot be deleted—the Delete button remains greyed out even though the camera is no longer attached to any configuration.

Inferences

You can't rename a running inference. Wait for the inference to stop or stop it manually before renaming.
If you stop an inference within the first minute of this first run, inference logs will not be available.
It is possible to delete a stopping inference from the project details page.
You cannot restart inferences in the Failed status. To proceed, stop the inference and start it again.
In an inference setup, repeatedly switching between imported models and then saving the setup may cause the platform to crash.
If your license expires, you cannot access the inference center to view details of inferences started while the license was active. Renew the license to regain access.
Inference monitoring:
- Opening a project with limited functionality from the Inference monitoring page results in an error. To open the project, go to the Projects module and search for the project directly.
- On the Inference monitoring page, the same inference metric type may be listed multiple times. This can occur after an upgrade when projects are updated to the new algorithm version, so both the old and new versions of the algorithm remain on the platform.
- Started inference appears as hidden on the Inference monitoring page. To make the inference visible, click the eye icon.

Import and export of data

If the import or export process is interrupted, it will restart from the beginning instead of resuming, potentially resulting in multiple notifications about the process start.
Data transfer from connected Robovision AI platform:
- On the target Robovision AI platform, you won't receive notifications about data upload.
- On the project details page of the target Robovision AI platform, you can't stop the data upload.
- Exporting large numbers of samples to the connected platform may fail. To avoid this, export no more than 200,000 samples at a time.
- If the bandwidth is below 8 Mbit, the export to the connected platform may fail.
- If you delete an import while it's still in progress, some imported samples may not be deleted. These samples will appear in the label center, but they won't be associated with any import.
- EfficientNet: During samples export, some samples may incorrectly show empty annotations. Once the data transfer is complete, the annotations will be updated, and the issue will no longer appear.
- When data upload is in progress, the number of samples displayed above the sample preview in the label center may be out of sync with the number shown in the Imports group of the Filter panel.
Inference setups:
- Imported models aren't displayed on the training details page and the Training center section of the project details page because trainings—not models—are shown there. However, you will still be able to select imported models for transfer learning, to set up a test, and more.
- YOLOv8+ instance segmentation: Import of inference setups with YOLOv11 models may fail, especially if the network is interrupted. In most cases, the issue resolves itself once the network stabilizes.
- You can import several models with the same name.
Samples:
- If you don't change the default name, all exports in your project will share the same name.
- Sample export or import, especially for more than 100 samples, may become stuck. To resolve this, manually resume the process.

Robovision AI 5.11¶

Release date: October 14, 2025

What's new¶

Camera configuration

To record data, perform calibration, or use your cameras for inference, you now need to create a camera configuration first. Camera configurations can include one or multiple cameras. The number and position of cameras are determined by the project type. See Camera management.

Smart mask tool for object detection

You can now perform one-click detection in object detection projects. Powered by the SAM (Segment Anything Model), the model can accurately predict object boundaries even in complex or noisy environments. See Smart mask tool.

Metadata filters

You can now create custom metadata filters – defining rules based on sample metadata (e.g., filename), thus having more flexibility beyond the standard project-specific options. See Filter samples.

AI-ADC test metrics

For the AI-ADC EfficientNet project, the test metrics have been extended. In addition to existing metrics, the following will now be displayed in the test center:

Unknown rate: Percentage of samples with the "unknown" class; equals 0 if no optimization option is selected during test setup.
Macro average precision: Average percentage of correct predictions across all classes, treating each class equally.
Macro average recall: Average percentage of actual samples correctly identified across all classes, treating each class equally.
Precision per class: Percentage of correct predictions for each individual class.
Recall per class: Percentage of actual samples correctly identified for each individual class.

Tags in data center

You can now manage tags in your project directly in the data center, making it easier to organize and categorize your data.

GPU support for classification projects

You can now choose to enable GPU usage when setting up inference for all classification projects.

SDK

A new version of the Robovision AI SDK is now available as a unified robovision_sdk package for easier installation and a clearer structure, enabling you to build and integrate your custom algorithm logic directly into the platform.

With the new SDK, you can already define your own algorithm for classification, segmentation, or object detection. Customize how it interacts with datasets and what API it exposes, in particular you can:

Implement a training job using your own logic and frameworks.
Add a custom testing job to evaluate your models.
Integrate your own inference deployment within Robovision AI, and expose an API just like with the built-in algorithms.

This gives you the flexibility to bring proprietary algorithms, domain-specific models, or experimental approaches into the platform while still leveraging the existing tools for project management, annotation, testing, and deployment.

See the SDK documentation for details on getting started. The password is ineedhelp.

Bug fixes¶

AI-ADC EfficientNet

The issue where KRF export failed has been resolved.
A performance degradation issue caused by disabled concurrency for predictions in PMI 5.10.0 has been fixed.