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updatedAt: 2026-09-28T14:29:25.000Z
agentTools:
projectIndex: https://documentation.picsellia.com/llms.txt
---
# Export as a model
At this stage, you should have already created several `Experiment`, executed the related trainings, and analyzed their results. You should now have selected the `Experiment` you consider a success and that suits your needs. The next step is to turn this `Experiment` into a `ModelVersion` stored in your *Private Registry* and ready for deployment.
## 1. Export as a Model
After accessing the `Experiment` you want to create a `ModelVersion` from, click on the *Export as a Model* button:
A modal will then open, offering two options:
* Create a brand-new `Model` and its very first `ModelVersion`.
* Create a new `ModelVersion` of an existing `Model`.
### Model versioning system
It is important to keep the Picsellia _Model versioning system_ in mind: as is the case for `Dataset`, a `Model` on Picsellia is composed of one or several `ModelVersion`.
The `ModelVersion` is what can actually be considered a model in the data science ecosystem, as the model files and all model properties are contained under a `ModelVersion`.
This _Model versioning system_ mainly aims at ensuring the model's traceability throughout its whole lifecycle and any potential evolution or improvement.
### A. New Model
To create a brand-new `Model` and its first `ModelVersion` from the current `Experiment`, use the *Create model* tab.
In this modal, you'll first be asked to initialize your new `Model` with a name and description, then initialize its first `ModelVersion` with a name and description. The `Model` and `ModelVersion` are created in your *Private Registry* once you click *Export*.
A good practice is to name and describe your `Model` after its actual detection purpose, and the `ModelVersion` with more specific details that might change in a further version — but naming and description conventions are ultimately up to you.
### B. New ModelVersion
To export the current `Experiment` as a new `ModelVersion` of a `Model` that already exists in your *Private Registry*, switch to the *Create model version* tab.
In this modal, select the existing `Model`, from your *Private Registry*, under which you want to create a new `ModelVersion` with the current `Experiment`. Then give a name and description to the new `ModelVersion`. It is created in your *Private Registry* once you click *Export*.
## 2. Exported items
Exporting an `Experiment` as a `ModelVersion` creates a new `ModelVersion` in your *Private Registry*, and this new object inherits several items from its source `Experiment`.
For more details about the `Model` and `ModelVersion` structure on Picsellia, see [Model versioning system](https://documentation.picsellia.com/docs/registry-model-versioning-system).
Note that if some items are missing from the `Experiment`, they won't be exported, and the related item in the exported `ModelVersion` will be empty — all these items are optional for both `Experiment` and `ModelVersion`, and their usage depends on your needs and how you use the Picsellia platform.
### A. Model Files
The most important item, which defines the `ModelVersion` itself, is the model file(s).
When exporting an `Experiment` as a new `ModelVersion`, Picsellia duplicates all the *Artifacts* related to the `Experiment` and copies them as model files attached to the exported `ModelVersion`.
### B. LabelMap
The `LabelMap` is also inherited by the exported `ModelVersion`.
To be precise, the `LabelMap` is initialized when creating an `Experiment`, based on the `Label` defined during `DatasetVersion` creation. When exporting the `Experiment` as a new `ModelVersion`, the `LabelMap` is inherited from the source `Experiment` and attached to the created `ModelVersion`.
The `LabelMap` of the `ModelVersion` remains editable, but editing it might compromise the consistency of your `ModelVersion`.
### C. Parameters
The parameters are also inherited from the `Experiment` by the exported `ModelVersion`, for two main purposes:
* Keep track, at the `ModelVersion` level, of the training parameters used to train the current `ModelVersion` in the frame of its source `Experiment`.
* Have the list of training parameters and their default values pre-filled in case the exported `ModelVersion` is used as *Base architecture* for a new `Experiment`.
### D. Docker Image
As with the training parameters, the Docker image is also inherited from the `Experiment` by the exported `ModelVersion`, again for two main purposes:
* Keep track, at the `ModelVersion` level, of the Docker image used to train the current `ModelVersion` in the frame of its source `Experiment`.
* Have the Docker image ready to use for the training script execution, in case the exported `ModelVersion` is used as *Base architecture* for a new `Experiment`.
### E. Source Experiment
To ensure the structure and consistency of your Computer Vision work — which aims at creating and operating the best-performing `ModelVersion` — it is essential to have a clear view of where your `ModelVersion` comes from.
This is why, when exporting an `Experiment` as a `ModelVersion` of your *Private Registry*, Picsellia ensures that you can retrieve, from your `ModelVersion`, the `Experiment` that gave birth to it.
### F. Dataset Versions
For the exact same reasons as for the source `Experiment`, from your `ModelVersion` you can retrieve the `DatasetVersion` used to train it.