# Classifying images
This tutorial shows you how to create the project and datasets for a model that classifies objects, scenes, and concepts found in an image. The model classifies the entire image. For example, by following this tutorial, you can train a model to recognize household locations such as a living room or kitchen. The tutorial also shows you how to use the model to analyze images.
Before starting the tutorial, we recommend that you read [Understanding Amazon Rekognition Custom Labels](understanding-custom-labels.md).
In this tutorial, you create the training and test datasets by uploading images from your local computer. Later you assign image-level labels to the images in your training and test datasets.
The model you create classifies images as belonging to the set of image-level labels that you assign to the training dataset images. For example, if the set of image-level labels in your training dataset is `kitchen`, `living_room`, `patio`, and `backyard`, the model can potentially find all of those image-level labels in a single image.
**Note**
You can create models for different purposes such as finding the location of objects on an image. For more information, see [Decide your model type](understanding-custom-labels.md#tm-intro-model-type).
## Step 1: Collect your images
You need two sets of images. One set to add to your training dataset. Another set to add to your test dataset. The images should represent the objects, scenes, and concepts that you want your model to classify. The images must be in PNG or JPEG format. For more information, see [Preparing images](md-prepare-images.md).
You should have at least 10 images for your training dataset and 10 images for your test dataset.
If you don't yet have images, use the images from the *Rooms* example classification project. After creating the project, the training and test images are at the following Amazon S3 bucket locations:
+ Training images — `s3://custom-labels-console-{{region}}-{{numbers}}/assets/rooms_{{version number}}_test_dataset/`
+ Test images — `s3://custom-labels-console-{{region}}-{{numbers}}/assets/rooms_{{version number}}_test_dataset/`
`region` is the AWS Region in which you are using the Amazon Rekognition Custom Labels console. `numbers` is a value that the console assigns to the bucket name. `Version number` is the version number for the example project, starting at 1.
The following procedure stores images from the Rooms project into local folders on your computer named `training` and `test`.
**To download the Rooms example project image files**
1. Create the Rooms project. For more information, see [Step 1: Choose an example project](gs-step-choose-example-project.md).
1. Open the command prompt and enter the following command to download the training images.
```
aws s3 cp s3://custom-labels-console-{{region}}-{{numbers}}/assets/rooms_{{version number}}_training_dataset/ training --recursive
```
1. At the commend prompt, enter the following command to download the test images.
```
aws s3 cp s3://custom-labels-console-{{region}}-{{numbers}}/assets/rooms_{{version number}}_test_dataset/ test --recursive
```
1. Move two of the images from the training folder to a separate folder of your choosing. You'll use the images to try your trained model in [Step 9: Analyze an image with your model](#tutorial-step-get-a-prediction).
## Step 2: Decide your classes
Make a list of the classes that you want your model to find. For example, if you're training a model to recognize rooms in a house, you can classify the following image as `living_room`.
Each class maps to an image-level label. Later you assign image-level labels to the images in your training and test datasets.
If you're using the images from the Rooms example project, the image-level labels are *backyard*, *bathroom*, *bedroom*, *closet*, *entry\_way*, *floor\_plan*, *front\_yard*, *kitchen*, *living\_space*, and *patio*.
## Step 3: Create a project
To manage your datasets and models you create a project. Each project should address a single use case, such as recognizing rooms in a house.
**To create a project (console)**
1. If you haven't already, set up the Amazon Rekognition Custom Labels console. For more information, see [Setting up Amazon Rekognition Custom Labels](setting-up.md).
1. Sign in to the AWS Management Console and open the Amazon Rekognition console at [https://console.aws.amazon.com/rekognition/](https://console.aws.amazon.com/rekognition/).
1. In the left pane, choose **Use Custom Labels**. The Amazon Rekognition Custom Labels landing page is shown.
1. The Amazon Rekognition Custom Labels landing page, choose **Get started**
1. In the left navigation pane, choose **Projects**.
1. On the projects page, choose **Create Project**.
1. In **Project name**, enter a name for your project.
1. Choose **Create project** to create your project.
## Step 4: Create training and test datasets
In this step you create a training dataset and a test dataset by uploading images from your local computer. You can upload as many as 30 images at a time. If you have a lot of images to upload, consider creating the datasets by importing the images from an Amazon S3 bucket. For more information, see [Importing images from an Amazon S3 bucket](md-create-dataset-s3.md).
For more information about datasets, see [Managing datasets](managing-dataset.md).
**To create a dataset using images on a local computer (console)**
1. On the project details page, choose **Create dataset**.
1. In the **Starting configuration** section, choose **Start with a training dataset and a test dataset**.
1. In the **Training dataset details** section, choose **Upload images from your computer**.
1. In the **Test dataset details** section, choose **Upload images from your computer**.
1. Choose **Create datasets**.
1. A dataset page appears with a **Training** tab and a **Test** tab for the respective datasets.
1. On the dataset page, choose the **Training** tab.
1. Choose **Actions** and then choose **Add images to training dataset**.
1. In the **Add images to training dataset** dialog box, choose **Choose files**.
1. Choose the images you want to upload to the dataset. You can upload as many as 30 images at a time.
1. Choose **Upload images**. It might take a few seconds for Amazon Rekognition Custom Labels to add the images to the dataset.
1. If you have more images to add to the training dataset, repeat steps 9-12.
1. Choose the **Test** tab.
1. Repeat steps 8 - 12 to add images to the test dataset. For step 8, choose **Actions** and then choose **Add images to test dataset**.
## Step 5: Add labels to the project
In this step you add a label to the project for each of the classes you identified in step [Step 2: Decide your classes](#tutorial-classify-images-decide-categories).
**To add a new label (console)**
1. On the dataset gallery page, choose **Start labeling** to enter labeling mode.
1. In the **Labels** section of the dataset gallery, choose **Edit labels** to open the **Manage labels** dialog box.
1. In the edit box, enter a new label name.
1. Choose **Add label**.
1. Repeat steps 3 and 4 until you have created all the labels you need.
1. Choose **Save** to save the labels that you added.
## Step 6: Assign image-level labels to training and test datasets
In this step you assign a single image level to each image in your training and test datasets. The image-level label is the class that each image represents.
**To assign image-level labels to an image (console)**
1. On the **Datasets** page, choose the **Training** tab.
1. Choose **Start labeling** to enter labeling mode.
1. Select one or more images that you want to add labels to. You can only select images on a single page at a time. To select a contiguous range of images on a page:
1. Select the first image.
1. Press and hold the shift key.
1. Select the second image. The images between the first and second image are also selected.
1. Release the shift key.
1. Choose **Assign image-level labels**.
1. In **Assign image-level labels to selected images** dialog box, select a label that you want to assign to the image or images.
1. Choose **Assign** to assign label to the image.
1. Repeat labeling until every image is annotated with the required labels.
1. Choose the **Test** tab.
1. Repeat steps to assign image level labels to the test dataset images.
## Step 7: Train your model
Use the following steps to train your model. For more information, see [Training an Amazon Rekognition Custom Labels model](training-model.md).
**To train your model (console)**
1. On the **Dataset** page, choose **Train model**.
1. On the **Train model** page, choose **Train model**. The Amazon Resource Name (ARN) for your project is in the **Choose project** edit box.
1. In the **Do you want to train your model?** dialog box, choose **Train model**.
1. In the **Models** section of the project page, you can see that training is in progress. You can check the current status by viewing the `Model Status` column for the model version. Training a model takes a while to complete.
1. After training completes, choose the model name. Training is finished when the model status is **TRAINING\_COMPLETED**.
1. Choose the **Evaluate** button to see the evaluation results. For information about evaluating a model, see [Improving a trained Amazon Rekognition Custom Labels model](improving-model.md).
1. Choose **View test results** to see the results for individual test images. For more information, see [Metrics for evaluating your model](im-metrics-use.md).
1. After viewing the test results, choose the model name to return to the model page.
## Step 8: Start your model
In this step you start your model. After your model starts, you can use it to analyze images.
You are charged for the amount of time that your model runs. Stop your model if you don't need to analyze images. You can restart your model at a later time. For more information, see [Running a trained Amazon Rekognition Custom Labels model](running-model.md).
**To start your model**
1. Choose the **Use model** tab on the model page.
1. In the **Start or stop model** section do the following:
1. Choose **Start**.
1. In the **Start model** dialog box, choose **Start**.
1. Wait until the model is running. The model is running when the status in the **Start or stop model** section is **Running**.
## Step 9: Analyze an image with your model
You analyze an image by calling the [DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels) API. In this step, you use the `detect-custom-labels` AWS Command Line Interface (AWS CLI) command to analyze an example image. You get the AWS CLI command from the Amazon Rekognition Custom Labels console. The console configures the AWS CLI command to use your model. You only need to supply an image that's stored in an Amazon S3 bucket.
**Note**
The console also provides Python example code.
The output from `detect-custom-labels` includes a list of labels found in the image, bounding boxes (if the model finds object locations), and the confidence that the model has in the accuracy of the predictions.
For more information, see [Analyzing an image with a trained model](detecting-custom-labels.md).
**To analyze an image (console)**
1. If you haven't already, set up the AWS CLI. For instructions, see [Step 4: Set up the AWS CLI and AWS SDKs](su-awscli-sdk.md).
1. Choose the **Use Model** tab and then choose **API code**.
1. Choose **AWS CLI command**.
1. In the **Analyze image** section, copy the AWS CLI command that calls `detect-custom-labels`.
1. Upload an image to an Amazon S3 bucket. For instructions, see [Uploading Objects into Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UploadingObjectsintoAmazonS3.html) in the *Amazon Simple Storage Service User Guide*. If you're using images from the Rooms project, use one of the images you moved to a separate folder in [Step 1: Collect your images](#tutorial-classify-images-collect-images).
1. At the command prompt, enter the AWS CLI command that you copied in the previous step. It should look like the following example.
The value of `--project-version-arn` should be Amazon Resource Name (ARN) of your model. The value of `--region` should be the AWS Region in which you created the model.
Change `MY_BUCKET` and `PATH_TO_MY_IMAGE` to the Amazon S3 bucket and image that you used in the previous step.
If you are using the [custom-labels-access](su-sdk-programmatic-access.md#su-sdk-programmatic-access-customlabels-examples) profile to get credentials, add the `--profile custom-labels-access` parameter.
```
aws rekognition detect-custom-labels \
--project-version-arn "{{model_arn}}" \
--image '{"S3Object": {"Bucket": "{{MY_BUCKET}}","Name": "{{PATH_TO_MY_IMAGE}}"}}' \
--region {{us-east-1}} \
--profile custom-labels-access
```
The JSON output from the AWS CLI command should look similar to the following. `Name` is the name of the image-level label that the model found. `Confidence` (0-100) is the model's confidence in the accuracy of the prediction.
```
{
"CustomLabels": [
{
"Name": "living_space",
"Confidence": 83.41299819946289
}
]
}
```
1. Continue to use the model to analyze other images. Stop the model if you are no longer using it.
## Step 10: Stop your model
In this step you stop running your model. You are charged for the amount of time your model is running. If you have finished using the model, you should stop it.
**To stop your model**
1. In the **Start or stop model** section choose **Stop**.
1. In the **Stop model** dialog box, enter **stop** to confirm that you want to stop the model.
1. Choose **Stop** to stop your model. The model has stopped when the status in the **Start or stop model** section is **Stopped**.
