|Transitioned dataset information handling for model version creation
- Previously, dataset info was only stored in
train_info.params.dataset_version_id . We included an additional check for
train_info.dataset.version in the model type fields, which take precedence if available.
- We also added two new field types,
DATASET_VERSION), to replace the older ID-based fields, enabling the use of actual dataset objects and facilitating compatibility with datasets from other applications in the future.
|Improved the "Use Model / Use Workflow" modal pop-up
|When you click either the "Use Model" or the "Use Workflow" button on the respective model's or workflow's page, a pop-up window will appear.
- We streamlined the user experience by placing the "Call by API" tab as the initial option within this window. Previously, the "Use in a Workflow / App" tab held this position, but we prioritized the more common "Call by API" functionality for easier access.
- We also enhanced accessibility by positioning Python as the primary option among the programming languages with code snippets.
- We also updated the code snippets for text models, setting raw text as the default option for making predictions. Predicting via local files and via URLs are still available as optional alternatives.
|Created new model versions for person detector models without cropping, as cropping is causing these models to miss people on the margins
- We duplicated the existing model versions and modified the data provider parameters to include downsampling, resizing, and padding only, in alignment with the standard upload process for new visual models.
|Improved the presentation of the JSON output generated from model predictions
- Previously, the JSON output would extend beyond the borders of the display modal screen, causing inconvenience.
- We also improved the user experience by making the button for copying all the output contents more user-friendly and intuitive.
|Improved the prediction area for Community models to show a "sign up or log in" prompt for users who are not currently logged in
- Previously, when you were not logged in, the Community model output section showed an "insufficient scopes" error. We now intercept the error and instead prompt users to log in or sign up—while showing the default predictions.
|Fixed an issue that caused an application to crash
- Previously, if you clicked the "Use Model" button and then selected the "Call by API" option for certain models, the application crashed. We fixed the issue.
|Fixed an issue where an unexpected pop-up window appeared while carrying out various actions
- The rogue pop-up interruption is no longer visible when adding models to workflows, when clicking the "Cancel" button while choosing the model path, or when creating a new app.
|Fixed an issue where it was not possible to view the evaluation metrics of old transfer learned models
- Previously, you could not access the evaluation metrics for older transfer learning models, as the drop-down menu lacked the option to select a dataset. That limitation applied to all transfer learning models that were trained and evaluated prior to the implementation of the changes on how the evaluation metrics work.
|Fixed an issue where the
base_model for transfer learning models did not display a list of the available base models
- All the models from the base workflow that produce embeddings are currently listed.