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updatedAt: 2026-09-28T14:18:59.000Z
agentTools:
projectIndex: https://documentation.picsellia.com/llms.txt
---
# Evaluation
The purpose of creating a `Project` and performing several `Experiment` in its frame is basically to train the best-performing `ModelVersion`. As detailed on the previous [page](https://documentation.picsellia.com/docs/experiment-experiment-tracking), you can log *Metrics* in the *Experiment Tracking dashboard* to get the insights needed to assess the quality of the training performed. In addition to the *Metrics*, Picsellia also lets you compare, on a dedicated bench of images, the predictions made by the freshly trained model with the ground truth — this is called an `Evaluation`.
By leveraging the *Evaluation interface*, you can get a precise overview of the model's behavior, get performance metrics on each evaluated image, and identify the contexts in which the model performs well or not.
With *Metrics* and `Evaluation`, you'll have all the insights needed to assess the quality of the trained `ModelVersion`, understand why it behaves the way it does, and identify ways to improve its performance in a further `Experiment`.
## 1. Create an Evaluation
`Evaluation` are computed and created by your training script. It needs to get a bunch of images unused during the training phase and already annotated in an existing `DatasetVersion`, in order to use them as ground truth.
This bunch of images can come from the training script splitting the `DatasetVersion` attached to the `Experiment`, or from a `DatasetVersion` dedicated to `Evaluation` and attached to the `Experiment` with an alias indicating its purpose.
The script then makes the just-trained `ModelVersion` infer on the evaluation images and sends the result to the Picsellia `Experiment`.
The outcome is that, for each evaluation image, you can compare the prediction made by the `ModelVersion` with the ground truth pulled from the original `DatasetVersion`. You'll also have access to evaluation metrics computed by Picsellia based on the comparison of predictions and ground truth.
The creation of `Evaluation` is already integrated into almost all the training scripts attached to `ModelVersion` from the *Public Registry*.
However, if you are using your own training script integrated with Picsellia, you can use this dedicated [tutorial](https://documentation.picsellia.com/docs/evaluate-your-model-performances), detailing how to create and log `Evaluation` within your Picsellia `Experiment`.
## 2. Evaluation interface
From an `Experiment`, you can access the *Evaluations* tab, as shown below:
After `Experiment` creation, the *Evaluation interface* is empty. It is only once the training script has been executed successfully that `Evaluation` are created in the *Evaluation interface*.
The *Evaluation interface* offers a way to visualize and explore `Evaluation` with the same philosophy and features as the `Datalake` to browse among data, or a `DatasetVersion` to browse among `Asset`.
### A. Evaluation visualization
#### a. Evaluation views and global navigation
You'll find three different views:
* *Grid view*, letting you visualize many `Evaluation` at a time.
* *Table view*, letting you quickly visualize many `Evaluation` and their associated information (filename, width, height, detection performances).
* *Details view*, letting you visualize one `Evaluation` in detail (full-screen image display and associated information).
The *Grid*, *Table*, and *Details* views, along with their navigation and display options, are in substance the same ones already covered for the `Datalake` (detailed [here](https://documentation.picsellia.com/docs/datalake-overview#2data-visualization)) and a `DatasetVersion` (detailed [here](https://documentation.picsellia.com/docs/dataset-overview#2-asset-visualization)).
#### b. Evaluation Shape
For each `Evaluation`, you can visualize the image, as well as the superposition of the prediction made by the trained `ModelVersion` and the ground truth inherited from the `DatasetVersion`.
The ground truth `Shape` are displayed in green, whereas the predicted `Shape` are red. For each `Shape`, the associated `Label` is displayed along with the confidence score in the case of predictions.
To ensure the smoothest visualization possible, you can select which elements to display on your image.
You can choose to display, on your `Evaluation` image, only the ground truth, only the predictions, or both. You can also filter the `Shape` to display by `Label` or by confidence score (in the case of predictions).
#### c. Evaluation metrics
As explained previously, for each `Evaluation`, Picsellia compares ground truth and prediction to compute different metrics, allowing you to assess the quality of the prediction made by the `ModelVersion` on that particular image.
On the *Details* view, besides the usual properties, `Metadata`, and `Custom Metadata`, some metrics computed by the platform are displayed to assess the performance of the model against the ground truth for each `Evaluation`.
The number of ground truth `Shape`, True Positives, False Positives, and False Negatives are displayed as shown below:
In addition, more in-depth metrics (AP/AR on several IoUs) can be visualized in the *Table* and *Details* views for each `Evaluation`:
### Not applicable for Classification
Those metrics are computed only for `Evaluation` performed in the frame of an `Experiment` with _Inference Type_ _Object Detection_ or _Segmentation_. For _Classification_, please refer to the dedicated section at the end of this page.
### B. Evaluation filtering
To let you investigate the `Evaluation` generated and understand the `ModelVersion`'s behavior in depth, `Evaluation` can be filtered on several criteria.
#### a. Date
The date picker lets you filter `Evaluation` on their creation date by clicking on the *Select Date* button. The date picker will open, letting you select the timeframe to search on.
Once the date range is selected, the associated query is filled in the search bar, letting you complete it directly through the search bar for a more specific query.
#### b. Metrics
The *Evaluation interface* also lets you filter on the computed metrics. To do so, click on *+ Add filter* and define, through the modal, the metric and the values to filter on. For instance, let's create a filter that displays only the `Evaluation` created between 09/19/2023 and 09/22/2023, with an Average Precision (IoU 50-95) between 0.291 and 0.782:
The images matching the active filters will be displayed.
Any filter can be removed at any time by clicking on it, then on the trash icon.
#### c. Custom Metadata
The *+ Add filter* modal also lets you filter `Evaluation` on the `Custom Metadata` of the underlying `Data` — the same key/value fields you can define on a `Data` in the `Datalake`. Pick the key you want to filter on — the keys declared in the `Custom Metadata` schema of the `Datalake` the underlying `Data` belongs to are suggested, detailed [here](https://documentation.picsellia.com/docs/datalake-metadata#3-the-custom-metadata-schema) — then its value type, and the value or range to match. This is useful to slice your evaluation bench along whatever `Custom Metadata` you already track on your `Data`, for instance filtering `Evaluation` down to the ones acquired by a specific sensor or under a specific condition, without needing an `Asset Attribute` or `Shape Attribute` for it.
#### d. Filter `Evaluations` by `AssetAttribute` and/or `ShapeAttribute`
The *+ Add filter* modal also lets you filter `Evaluation` on `Attribute` values — those inherited from the ground truth `Asset`, and those carried by its `Shape` — as well as on the `Shape` themselves, by their type or their `Label`. This is a direct way to check the `ModelVersion` behavior on a specific slice of the evaluation bench, for instance only the images whose `Asset Attribute` marks a difficult acquisition condition. `Attribute` filtering is detailed [here](https://documentation.picsellia.com/docs/dataset-attributes#5-filter-on-attribute-values).
### Summary on filters
In summary, several types of filters can be combined to browse your `Evaluation` based on multiple criteria — creation date, average precision, average recall, `Custom Metadata`, `Asset Attribute`, or `Shape Attribute` — alongside the _Query Language_ (detailed below), to refine your search even further.
### C. Search Bar
As with all the image visualization views available on Picsellia, you have access to the search bar powered by our *Query Language*. This search bar lets you create complex queries leveraging all the properties related to each `Evaluation`.
The *Query Language* lets you browse among properties of the image itself (linked to an `Asset`), of the ground truth `Annotation`, or of the `Evaluation` `Annotation`. You can rely on auto-completion to see all the properties you can search on.
To search on the `Evaluation` `Annotation`, you need to type *polygons.xxx* (or the current shape type), whereas if you want to filter on the ground truth `Annotation`, you need to access the `Asset` first and type *asset.polygons.xxx*.
### D. Evaluation deletion
To delete any computed `Evaluation`, select the `Evaluation` to delete and click on *Delete*.
### E. Embeddings and visual search
Embeddings can also be computed for the `Evaluation` of an `Experiment`, the same way as for the `Data` of a `Datalake` (detailed [here](https://documentation.picsellia.com/docs/datalake-explore-your-data-with-embeddings)). On an evaluation bench of a few thousand images, this is how you find the images that resemble the one the `ModelVersion` failed on, instead of scrolling until you meet them.
The computation is activated from the *Data Embeddings* page of the `Experiment` *Settings*, where you can also follow its progress, *Retry* the `Evaluation` that could not be computed, and *Deactivate* the computation, which deletes the embeddings already computed.
Once the computation is over, you can look for the `Evaluation` that look like a selected one, or that match a text prompt.
You can also switch to the *Embeddings* exploration mode to visualize a UMAP projection of every `Evaluation` embedding as a scatter plot, automatically grouped into clusters of visually similar images. Selecting a cluster with the lasso, rectangle, or cluster-selection tool highlights the corresponding `Evaluation` in the grid — a fast way to check whether a cluster of visually similar images shares the same kind of ground truth/prediction mismatch, instead of inspecting `Evaluation` one by one.
### F. Charts
Alongside *Query Language* and *Embeddings*, a third exploration mode, *Charts*, lets you build aggregate visualizations over your `Evaluation` — for instance, the average precision of your `Evaluation`, grouped by `DataTag`, to spot which subsets of your data the `ModelVersion` struggles with. Creating and managing a `Chart` is detailed in [Charts](https://documentation.picsellia.com/docs/experiment-evaluation-charts).
### G. Compare across Experiment
Everything so far looks at the `Evaluation` of a single `Experiment`. If two or more `Experiment` share a common evaluated `DatasetVersion`, Picsellia also lets you overlay their `Evaluation` on the same `Asset`, to see directly, image by image, how their predictions differ. This is detailed in [Comparison](https://documentation.picsellia.com/docs/experiment-comparison#2-compare-evaluations).
## 3. Classification
The case of *Classification* is handled a bit differently than *Object Detection* or *Segmentation* in the *Evaluation interface*. This is mainly because average recall and average precision metrics don't make sense for *Classification* `Experiment`.
The creation of `Evaluation` for *Classification* by a training script is detailed [here](https://documentation.picsellia.com/docs/evaluate-a-classification-model).
Most of the features remain the same as detailed previously.
The visualization of ground truth and prediction on each `Evaluation` follows the same guideline, with the ground truth `Label` on the left and the predicted `Label` on the right.
The other major difference lies in the computed metrics. For *Classification* `Evaluation`, it is the confidence score (referred to as *Score* in the *Evaluation interface*) of the prediction that is displayed and usable to create filters.
The ground truth and predicted `Label` are also displayed as fields in the *Table* and *Details* views.
To quickly access the `Evaluation` where the ground truth `Label` differs from the predicted one, click on the *Show class errors* button, available once the *Filters* bar has been opened.
For instance, in the screenshot above, using the *Show class errors* button shows only the `Evaluation` where the ground truth `Label` differs from the predicted one — in this case, 53 `Evaluation` out of 279 performed.