# Image Explanations

Understanding how complex models make predictions is crucial for ensuring transparency, building trust, and identifying potential biases. Model explainers provide insights into how features influence outcomes, aiding in debugging and refining models.

In this demonstration, you can learn about using [Alibi Explain's Anchor Images](/seldon-enterprise-platform/product-tour/model-explanations.md) method to explore model explanations. This includes identifying the segments of an input image that had the most influence on the prediction and analyzing the precision of the Anchor and Coverage metrics.

This demo helps you learn about:

* Launching an image classification pipeline
* Sending prediction requests to the pipeline
* Creating an explainer for the pipeline
* Generating explanations for previously sent prediction requests

The model used in this demo is already trained to classify images based on the [CIFAR10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).

## Create Seldon Pipeline

1. In the **Overview** page click **Create new deployment**.
2. Enter the following details for the deployment:
   * name: *cifar10-classifier*
   * namespace: *seldon*
   * Type: *Seldon ML Pipeline*

![Deployment details](/files/TLRIDvThJBV2Zv640dfD)

3. Configure the default predictor as follows:

   * Runtime: *Tensorflow*
   * Model Project: *default*
   * Model URI:

   ```
    gs://seldon-models/triton/tf_cifar10
   ```

   * Storage Secret: *(leave blank/none)*
4. Click **Next** for the remaining step and click **Launch**.
5. When your deployment is launched successfully, the status of the deployment becomes `Available`.

## Make Predictions

You can make a prediction request using the image of a frog from the cifar10 dataset. The image is a JSON file in the REST format of the [Open Inference Protocol](https://docs.seldon.io/projects/seldon-core/en/v2/contents/apis/inference/v2.html).

{% file src="/files/JmmJNWFu9YxtjZuOfZCu" %}

1. In the **Overview** page click the **cifar10-classifier** pipeline that you created.
2. In the deployment dashboard, click **Predict** in the left pane.
3. In the **Predict** page, click **Browse** and upload the `cifar10-frog-oip.json` file.
4. Click **Predict**.

![Successful prediction using a JSON file](/files/Jze2YIAdi4TJdtMktsg5)

## Add an Anchor Images Explainer

1. In the `cifar10-classifier` deployment dashboard, click **Add** inside the **MODEL EXPLANATION** card..
2. In the **Explainer Configuration Wizard**, choose **Image** and click **Next**.

![Explainer Model Data Type](/files/UYxGzZ4M7OsoZUaMOQpq)

3. In the **Explainer Types** step, choose the **Anchor** option for **Explainer Algorithms supported** and click **Next**.
4. In the **Explainer URI** step, set the following details:
   * Explainer URI: `gs://seldon-models/tfserving/cifar10/cifar10_anchor_image_py3.7_alibi-0.7.0`
   * Model Project: *default*
   * Storage Secret: *(leave blank/none)*

![Explainer URI](/files/nYfrt1B6NZzQrwuTfQ0L)

5. Click **Next** for the remaining steps without changing any fields, and click **Launch**.

   After sometime, the explainer should become available.

## Get an explanation for the Request

1. In the `cifar10-classifier` deployment dashboard, click **Requests** in the left pane.
2. Click the **View explanation** button to generate explanations for the request.

![Previously made prediction request with its predicted response](/files/eg0VAEVTVENMu6slLnDh)

After sometime the explanation for the requests is displayed.

![The frog image segments that influenced the prediction the most](/files/jMsZXzU3K9K7fHsSD53p)

![The Precision and Coverage Anchor metrics](/files/iw6ixLqqnY2hmHY8KkbJ)

![Perturbed samples of the prediction request which comply with the prediction](/files/ckCTRTYPUzssUPxjZ2oo)

## Next Steps

Try the other [demos](/seldon-enterprise-platform/demos.md) or read our [operations guide](/seldon-enterprise-platform/operations.md) to learn more about how to use Seldon Enterprise Platform.


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