# Tutorial: Run inference on an object table by using a classification model
This tutorial shows you how to create an object table based on the images
from a public dataset,
and then run inference on that object table using the
[ResNet 50 model](https://tfhub.dev/tensorflow/resnet_50/classification/1).
## The ResNet 50 model
The ResNet 50 model analyzes image files and outputs a batch of vectors
representing the likelihood that an image belongs to the corresponding class
(logits). For more information, see the **Usage** section on the
[model's TensorFlow Hub page](https://tfhub.dev/tensorflow/resnet_50/classification/1).
The ResNet 50 model input takes a tensor of
[`DType`](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType) =
`float32` in the shape `[-1, 224, 224, 3]`. The output is an array of
tensors of `tf.float32` in the shape`[-1, 1024]`.
## Required permissions
- To create the dataset, you need the `bigquery.datasets.create` permission.
- To create the connection resource, you need the following permissions:
- `bigquery.connections.create`
- `bigquery.connections.get`
- To grant permissions to the connection's service account, you need the
following permission:
- `resourcemanager.projects.setIamPolicy`
- To create the object table, you need the following permissions:
- `bigquery.tables.create`
- `bigquery.tables.update`
- `bigquery.connections.delegate`
- To create the bucket, you need the `storage.buckets.create` permission.
- To upload the model to Cloud Storage, you need the
`storage.objects.create` and `storage.objects.get` permissions.
- To load the model into BigQuery ML, you need the following
permissions:
- `bigquery.jobs.create`
- `bigquery.models.create`
- `bigquery.models.getData`
- `bigquery.models.updateData`
- To run inference, you need the following permissions:
- `bigquery.tables.getData` on the object table
- `bigquery.models.getData` on the model
- `bigquery.jobs.create`
## Costs
In this document, you use the following billable components of Google Cloud:
- **BigQuery**: You incur storage costs for the object table you create in BigQuery.
- **BigQuery ML**: You incur costs for the model you create and the inference you perform in BigQuery ML.
- **Cloud Storage**: You incur costs for the objects you store in Cloud Storage.
To generate a cost estimate based on your projected usage,
use the [pricing calculator](https://docs.cloud.google.com/products/calculator).
New Google Cloud users might be eligible for a [free trial](https://docs.cloud.google.com/free).
For more information on BigQuery storage pricing, see
[Storage pricing](https://cloud.google.com/bigquery/pricing#storage) in the BigQuery
documentation.
For more information on BigQuery ML pricing, see
[BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml) in
the BigQuery documentation.
For more information on Cloud Storage pricing, see the
[Cloud Storage pricing](https://cloud.google.com/storage/pricing) page.
## Before you begin
### Create a reservation
To use an
[imported model](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/inference-overview#inference_using_imported_models)
with an object table, you must
[create a reservation](https://docs.cloud.google.com/bigquery/docs/reservations-tasks#create_reservations)
that uses the BigQuery
[Enterprise or Enterprise Plus edition](https://docs.cloud.google.com/bigquery/docs/editions-intro),
and then
[create a reservation assignment](https://docs.cloud.google.com/bigquery/docs/reservations-assignments#create_reservation_assignments)
that uses the `QUERY` job type.
## Create a dataset
Create a dataset named `resnet_inference_test`:
### SQL
1. Go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the **Editor** pane, run the following SQL statement:
```googlesql
CREATE SCHEMA `PROJECT_ID.resnet_inference_test`;
```
Replace `PROJECT_ID` with your project ID.
### bq
1. In the Google Cloud console, activate Cloud Shell.
[Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true)
2. Run the
[`bq mk` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#mk-dataset)
to create the dataset:
```bash
bq mk --dataset --location=us PROJECT_ID:resnet_inference_test
```
Replace `PROJECT_ID` with your project ID.
## Create a connection
Create a connection named `lake-connection`:
### Console
1. Go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the left pane, click database **Data Explorer**:
If you don't see the left pane, click **Expand left pane** to open the pane.
3. In the **Data** pane, click **Add data**.
The **Add data** dialog opens.
4. In the **Filter By** pane, in the **Data Source Type** section, select **Databases**.
Alternatively, in the **Search for data sources** field, you can enter
`Vertex AI`.
5. In the **Featured data sources** section, click **Vertex AI**.
6. Click the **Vertex AI Models: BigQuery Federation** solution card.
7. In the **Connection type** list, select
**Vertex AI remote models, remote functions, BigLake and Spanner (Cloud Resource)**.
8. In the **Connection ID** field, type `lake-connection`.
9. Click **Create connection**.
10. In the **Connection info** pane, copy the value from the
**Service account ID** field and save it somewhere. You need this
information to [grant permissions](https://docs.cloud.google.com/bigquery/docs/inference-tutorial-resnet#grant-permissions) to the connection's
service account.
### bq
1. In Cloud Shell, run the
[`bq mk` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#mk-connection)
to create the connection:
bq mk --connection --location=us --connection_type=CLOUD_RESOURCE \
lake-connection
2. Run the [`bq show` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#bq_show)
to retrieve information about the connection:
bq show --connection us.lake-connection
3. From the `properties` column, copy the value of the `serviceAccountId`
property and save it somewhere. You need this information to
[grant permissions](https://docs.cloud.google.com/bigquery/docs/inference-tutorial-resnet#grant-permissions) to the connection's
service account.
## Create a Cloud Storage bucket
[Create a Cloud Storage bucket](https://docs.cloud.google.com/storage/docs/creating-buckets) to
contain the model files.
## Grant permissions to the connection's service account
### Console
1. Go to the **IAM \& Admin** page.
[Go to IAM \& Admin](https://console.cloud.google.com/project/_/iam-admin)
2. Click **Grant Access**.
The **Add principals** dialog opens.
3. In the **New principals** field, enter the service account ID that you
copied earlier.
4. In the **Select a role** field, select **Cloud Storage** , and then
select **Storage Object Viewer**.
5. Click **Save**.
### gcloud
In Cloud Shell, run the
[`gcloud storage buckets add-iam-policy-binding` command](https://docs.cloud.google.com/sdk/gcloud/reference/storage/buckets/add-iam-policy-binding):
```bash
gcloud storage buckets add-iam-policy-binding gs://BUCKET_NAME \
--member=serviceAccount:MEMBER \
--role=roles/storage.objectViewer
```
Replace `MEMBER` with the service account ID that you
copied earlier. Replace `BUCKET_NAME` with the name
of the bucket you previously created.
For more information, see [Add a principal to a bucket-level
policy](https://docs.cloud.google.com/storage/docs/access-control/using-iam-permissions#bucket-add).
> [!NOTE]
> **Note:** There can be a delay of up to a minute before new permissions take effect.
## Create an object table
Create an object table named `vision_images` based on the
image files in the public `gs://cloud-samples-data/vision` bucket:
### SQL
1. Go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the **Editor** pane, run the following SQL statement:
```googlesql
CREATE EXTERNAL TABLE resnet_inference_test.vision_images
WITH CONNECTION `us.lake-connection`
OPTIONS(
object_metadata = 'SIMPLE',
uris = ['gs://cloud-samples-data/vision/*.jpg']
);
```
### bq
In Cloud Shell, run the
[`bq mk` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#mk-table)
to create the connection:
bq mk --table \
--external_table_definition='gs://cloud-samples-data/vision/*.jpg@us.lake-connection' \
--object_metadata=SIMPLE \
resnet_inference_test.vision_images
## Upload the model to Cloud Storage
Get the model files and make them available in Cloud Storage:
1. [Download](https://tfhub.dev/tensorflow/resnet_50/classification/1?tf-hub-format=compressed) the ResNet 50 model to your local machine. This gives you a `saved_model.pb` file and a `variables` folder for the model.
2. [Upload](https://docs.cloud.google.com/storage/docs/uploading-objects) the `saved_model.pb` file and the `variables` folder to the bucket you previously created.
## Load the model into BigQuery ML
1. Go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the **Editor** pane, run the following SQL statement:
```googlesql
CREATE MODEL `resnet_inference_test.resnet`
OPTIONS(
model_type = 'TENSORFLOW',
model_path = 'gs://BUCKET_NAME/*');
```
Replace `BUCKET_NAME` with the name of the bucket
you previously created.
## Inspect the model
Inspect the uploaded model to see what its input and output fields are:
1. Go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the left pane, click **Explorer**:
3. In the **Explorer** pane, expand your project, click **Datasets** , and then
click the `resnet_inference_test` dataset.
4. Go to the **Models** tab.
5. Click the `resnet` model.
6. In the model pane that opens, click the **Schema** tab.
7. Look at the **Labels** section. This identifies the fields that are output
by the model. In this case, the field name value is
`activation_49`.
8. Look at the **Features** section. This identifies the fields that must
be input into the model. You reference them in the `SELECT` statement
for the `ML.DECODE_IMAGE` function. In this case, the field name value is
`input_1`.
## Run inference
Run inference on the `vision_images` object table using the `resnet` model:
1. Go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the **Editor** pane, run the following SQL statement:
```googlesql
SELECT *
FROM ML.PREDICT(
MODEL `resnet_inference_test.resnet`,
(SELECT uri, ML.RESIZE_IMAGE(ML.DECODE_IMAGE(data), 224, 224, FALSE) AS input_1
FROM resnet_inference_test.vision_images)
);
```
The results should look similar to the following:
---
| activation_49 | uri | input_1 |
---
| 1.0254175464297077e-07 | gs://cloud-samples-data/vision/automl_classification/flowers/daisy/21652746_cc379e0eea_m.jpg | 0.0 |
---
| 2.1671139620593749e-06 | | 0.0 |
--- ---
| 8.346052027263795e-08 | | 0.0 |
--- ---
| 1.159310958342985e-08 | | 0.0 |
---
## Clean up
> [!CAUTION]
> **Caution** : Deleting a project has the following effects:
>
> - **Everything in the project is deleted.** If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
> - **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an `appspot.com` URL, delete selected resources inside the project instead of deleting the whole project.
>
>
> If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects
> can help you avoid exceeding project quota limits.
1. In the Google Cloud console, go to the **Manage resources** page.
[Go to Manage resources](https://console.cloud.google.com/iam-admin/projects)
2. In the project list, select the project that you want to delete, and then click **Delete**.
3. In the dialog, type the project ID, and then click **Shut down** to delete the project.