Tutorial: Train an object detection model (preview) with AutoML and Python (v1)

Article
08/28/2024

Important

The features presented in this article are in preview. They should be considered experimental preview features that might change at any time.

In this tutorial, you learn how to train an object detection model using Azure Machine Learning automated ML with the Azure Machine Learning Python SDK. This object detection model identifies whether the image contains objects, such as a can, carton, milk bottle, or water bottle.

Automated ML accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.

You'll write code using the Python SDK in this tutorial and learn the following tasks:

Download and transform data
Train an automated machine learning object detection model
Specify hyperparameter values for your model
Perform a hyperparameter sweep
Deploy your model
Visualize detections

Prerequisites

If you don't have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning today.
Python 3.7 or 3.8 are supported for this feature
Complete the Quickstart: Get started with Azure Machine Learning if you don't already have an Azure Machine Learning workspace.
Download and unzip the *odFridgeObjects.zip data file. The dataset is annotated in Pascal VOC format, where each image corresponds to an xml file. Each xml file contains information on where its corresponding image file is located and also contains information about the bounding boxes and the object labels. In order to use this data, you first need to convert it to the required JSONL format as seen in the Convert the downloaded data to JSONL section of the notebook.

This tutorial is also available in the azureml-examples repository on GitHub if you wish to run it in your own local environment. To get the required packages,

Run pip install azureml
Install the full automl client

Compute target setup

You first need to set up a compute target to use for your automated ML model training. Automated ML models for image tasks require GPU SKUs.

This tutorial uses the NCsv3-series (with V100 GPUs) as this type of compute target leverages multiple GPUs to speed up training. Additionally, you can set up multiple nodes to take advantage of parallelism when tuning hyperparameters for your model.

The following code creates a GPU compute of size Standard _NC24s_v3 with four nodes that are attached to the workspace, ws.

Warning

Ensure your subscription has sufficient quota for the compute target you wish to use.

from azureml.core.compute import AmlCompute, ComputeTarget

cluster_name = "gpu-nc24sv3"

try:
    compute_target = ComputeTarget(workspace=ws, name=cluster_name)
    print('Found existing compute target.')
except KeyError:
    print('Creating a new compute target...')
    compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC24s_v3',
                                                           idle_seconds_before_scaledown=1800,
                                                           min_nodes=0,
                                                           max_nodes=4)

    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)

#If no min_node_count is provided, the scale settings are used for the cluster.
compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)

Experiment setup

Next, create an Experiment in your workspace to track your model training runs.


from azureml.core import Experiment

experiment_name = 'automl-image-object-detection'
experiment = Experiment(ws, name=experiment_name)

Visualize input data

Once you have the input image data prepared in JSONL (JSON Lines) format, you can visualize the ground truth bounding boxes for an image. To do so, be sure you have matplotlib installed.

%pip install --upgrade matplotlib


%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
from PIL import Image as pil_image
import numpy as np
import json
import os

def plot_ground_truth_boxes(image_file, ground_truth_boxes):
    # Display the image
    plt.figure()
    img_np = mpimg.imread(image_file)
    img = pil_image.fromarray(img_np.astype("uint8"), "RGB")
    img_w, img_h = img.size

    fig,ax = plt.subplots(figsize=(12, 16))
    ax.imshow(img_np)
    ax.axis("off")

    label_to_color_mapping = {}

    for gt in ground_truth_boxes:
        label = gt["label"]

        xmin, ymin, xmax, ymax =  gt["topX"], gt["topY"], gt["bottomX"], gt["bottomY"]
        topleft_x, topleft_y = img_w * xmin, img_h * ymin
        width, height = img_w * (xmax - xmin), img_h * (ymax - ymin)

        if label in label_to_color_mapping:
            color = label_to_color_mapping[label]
        else:
            # Generate a random color. If you want to use a specific color, you can use something like "red".
            color = np.random.rand(3)
            label_to_color_mapping[label] = color

        # Display bounding box
        rect = patches.Rectangle((topleft_x, topleft_y), width, height,
                                 linewidth=2, edgecolor=color, facecolor="none")
        ax.add_patch(rect)

        # Display label
        ax.text(topleft_x, topleft_y - 10, label, color=color, fontsize=20)

    plt.show()

def plot_ground_truth_boxes_jsonl(image_file, jsonl_file):
    image_base_name = os.path.basename(image_file)
    ground_truth_data_found = False
    with open(jsonl_file) as fp:
        for line in fp.readlines():
            line_json = json.loads(line)
            filename = line_json["image_url"]
            if image_base_name in filename:
                ground_truth_data_found = True
                plot_ground_truth_boxes(image_file, line_json["label"])
                break
    if not ground_truth_data_found:
        print("Unable to find ground truth information for image: {}".format(image_file))

def plot_ground_truth_boxes_dataset(image_file, dataset_pd):
    image_base_name = os.path.basename(image_file)
    image_pd = dataset_pd[dataset_pd['portable_path'].str.contains(image_base_name)]
    if not image_pd.empty:
        ground_truth_boxes = image_pd.iloc[0]["label"]
        plot_ground_truth_boxes(image_file, ground_truth_boxes)
    else:
        print("Unable to find ground truth information for image: {}".format(image_file))

Using the above helper functions, for any given image, you can run the following code to display the bounding boxes.

image_file = "./odFridgeObjects/images/31.jpg"
jsonl_file = "./odFridgeObjects/train_annotations.jsonl"

plot_ground_truth_boxes_jsonl(image_file, jsonl_file)

Upload data and create dataset

In order to use the data for training, upload it to your workspace via a datastore. The datastore provides a mechanism for you to upload or download data, and interact with it from your remote compute targets.

ds = ws.get_default_datastore()
ds.upload(src_dir='./odFridgeObjects', target_path='odFridgeObjects')

Once uploaded to the datastore, you can create an Azure Machine Learning dataset from the data. Datasets package your data into a consumable object for training.

The following code creates a dataset for training. Since no validation dataset is specified, by default 20% of your training data is used for validation.

from azureml.core import Dataset
from azureml.data import DataType

training_dataset_name = 'odFridgeObjectsTrainingDataset'
if training_dataset_name in ws.datasets:
    training_dataset = ws.datasets.get(training_dataset_name)
    print('Found the training dataset', training_dataset_name)
else:
    # create training dataset
        # create training dataset
    training_dataset = Dataset.Tabular.from_json_lines_files(
        path=ds.path('odFridgeObjects/train_annotations.jsonl'),
        set_column_types={"image_url": DataType.to_stream(ds.workspace)},
    )
    training_dataset = training_dataset.register(workspace=ws, name=training_dataset_name)

print("Training dataset name: " + training_dataset.name)

Visualize dataset

You can also visualize the ground truth bounding boxes for an image from this dataset.

Load the dataset into a pandas dataframe.

import azureml.dataprep as dprep

from azureml.dataprep.api.functions import get_portable_path

# Get pandas dataframe from the dataset
dflow = training_dataset._dataflow.add_column(get_portable_path(dprep.col("image_url")),
                                              "portable_path", "image_url")
dataset_pd = dflow.to_pandas_dataframe(extended_types=True)

For any given image, you can run the following code to display the bounding boxes.

image_file = "./odFridgeObjects/images/31.jpg"
plot_ground_truth_boxes_dataset(image_file, dataset_pd)

Configure your object detection experiment

To configure automated ML runs for image-related tasks, use the AutoMLImageConfig object. In your AutoMLImageConfig, you can specify the model algorithms with the model_name parameter and configure the settings to perform a hyperparameter sweep over a defined parameter space to find the optimal model.

In this example, we use the AutoMLImageConfig to train an object detection model with yolov5 and fasterrcnn_resnet50_fpn, both of which are pretrained on COCO, a large-scale object detection, segmentation, and captioning dataset that contains over thousands of labeled images with over 80 label categories.

Hyperparameter sweeping for image tasks

You can perform a hyperparameter sweep over a defined parameter space to find the optimal model.

The following code, defines the parameter space in preparation for the hyperparameter sweep for each defined algorithm, yolov5 and fasterrcnn_resnet50_fpn. In the parameter space, specify the range of values for learning_rate, optimizer, lr_scheduler, etc., for AutoML to choose from as it attempts to generate a model with the optimal primary metric. If hyperparameter values are not specified, then default values are used for each algorithm.

For the tuning settings, use random sampling to pick samples from this parameter space by importing the GridParameterSampling, RandomParameterSampling and BayesianParameterSampling classes. Doing so, tells automated ML to try a total of 20 iterations with these different samples, running four iterations at a time on our compute target, which was set up using four nodes. The more parameters the space has, the more iterations you need to find optimal models.

The Bandit early termination policy is also used. This policy terminates poor performing configurations; that is, those configurations that are not within 20% slack of the best performing configuration, which significantly saves compute resources.

from azureml.train.hyperdrive import RandomParameterSampling
from azureml.train.hyperdrive import BanditPolicy, HyperDriveConfig
from azureml.train.hyperdrive import choice, uniform

parameter_space = {
    'model': choice(
        {
            'model_name': choice('yolov5'),
            'learning_rate': uniform(0.0001, 0.01),
            #'model_size': choice('small', 'medium'), # model-specific
            'img_size': choice(640, 704, 768), # model-specific
        },
        {
            'model_name': choice('fasterrcnn_resnet50_fpn'),
            'learning_rate': uniform(0.0001, 0.001),
            #'warmup_cosine_lr_warmup_epochs': choice(0, 3),
            'optimizer': choice('sgd', 'adam', 'adamw'),
            'min_size': choice(600, 800), # model-specific
        }
    )
}

tuning_settings = {
    'iterations': 20,
    'max_concurrent_iterations': 4,
    'hyperparameter_sampling': RandomParameterSampling(parameter_space),
    'policy': BanditPolicy(evaluation_interval=2, slack_factor=0.2, delay_evaluation=6)
}

Once the parameter space and tuning settings are defined, you can pass them into your AutoMLImageConfig object and then submit the experiment to train an image model using your training dataset.

from azureml.train.automl import AutoMLImageConfig
automl_image_config = AutoMLImageConfig(task='image-object-detection',
                                        compute_target=compute_target,
                                        training_data=training_dataset,
                                        validation_data=validation_dataset,
                                        primary_metric='mean_average_precision',
                                        **tuning_settings)

automl_image_run = experiment.submit(automl_image_config)
automl_image_run.wait_for_completion(wait_post_processing=True)

When doing a hyperparameter sweep, it can be useful to visualize the different configurations that were tried using the HyperDrive UI. You can navigate to this UI by going to the 'Child runs' tab in the UI of the main automl_image_run from above, which is the HyperDrive parent run. Then you can go into the 'Child runs' tab of this one. Alternatively, here below you can see directly the HyperDrive parent run and navigate to its 'Child runs' tab:

from azureml.core import Run
hyperdrive_run = Run(experiment=experiment, run_id=automl_image_run.id + '_HD')
hyperdrive_run

Register the best model

Once the run completes, we can register the model that was created from the best run.

best_child_run = automl_image_run.get_best_child()
model_name = best_child_run.properties['model_name']
model = best_child_run.register_model(model_name = model_name, model_path='outputs/model.pt')

Deploy model as a web service

Once you have your trained model, you can deploy the model on Azure. You can deploy your trained model as a web service on Azure Container Instances (ACI) or Azure Kubernetes Service (AKS). ACI is the perfect option for testing deployments, while AKS is better suited for high-scale, production usage.

In this tutorial, we deploy the model as a web service in AKS.

Create an AKS compute cluster. In this example, a GPU virtual machine SKU is used for the deployment cluster

from azureml.core.compute import ComputeTarget, AksCompute
from azureml.exceptions import ComputeTargetException

# Choose a name for your cluster
aks_name = "cluster-aks-gpu"

# Check to see if the cluster already exists
try:
    aks_target = ComputeTarget(workspace=ws, name=aks_name)
    print('Found existing compute target')
except ComputeTargetException:
    print('Creating a new compute target...')
    # Provision AKS cluster with GPU machine
    prov_config = AksCompute.provisioning_configuration(vm_size="STANDARD_NC6",
                                                        location="eastus2")
    # Create the cluster
    aks_target = ComputeTarget.create(workspace=ws,
                                      name=aks_name,
                                      provisioning_configuration=prov_config)
    aks_target.wait_for_completion(show_output=True)

Define the inference configuration that describes how to set up the web-service containing your model. You can use the scoring script and the environment from the training run in your inference config.

Note

To change the model's settings, open the downloaded scoring script and modify the model_settings variable before deploying the model.
```
from azureml.core.model import InferenceConfig

best_child_run.download_file('outputs/scoring_file_v_1_0_0.py', output_file_path='score.py')
environment = best_child_run.get_environment()
inference_config = InferenceConfig(entry_script='score.py', environment=environment)
```

You can then deploy the model as an AKS web service.


from azureml.core.webservice import AksWebservice
from azureml.core.webservice import Webservice
from azureml.core.model import Model
from azureml.core.environment import Environment

aks_config = AksWebservice.deploy_configuration(autoscale_enabled=True,
                                                cpu_cores=1,
                                                memory_gb=50,
                                                enable_app_insights=True)

aks_service = Model.deploy(ws,
                           models=[model],
                           inference_config=inference_config,
                           deployment_config=aks_config,
                           deployment_target=aks_target,
                           name='automl-image-test',
                           overwrite=True)
aks_service.wait_for_deployment(show_output=True)
print(aks_service.state)

Test the web service

You can test the deployed web service to predict new images. For this tutorial, pass a random image from the dataset and pass it to the scoring URI.

import requests

# URL for the web service
scoring_uri = aks_service.scoring_uri

# If the service is authenticated, set the key or token
key, _ = aks_service.get_keys()

sample_image = './test_image.jpg'

# Load image data
data = open(sample_image, 'rb').read()

# Set the content type
headers = {'Content-Type': 'application/octet-stream'}

# If authentication is enabled, set the authorization header
headers['Authorization'] = f'Bearer {key}'

# Make the request and display the response
resp = requests.post(scoring_uri, data, headers=headers)
print(resp.text)

Visualize detections

Now that you have scored a test image, you can visualize the bounding boxes for this image. To do so, be sure you have matplotlib installed.

%pip install --upgrade matplotlib

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
from PIL import Image
import numpy as np
import json

IMAGE_SIZE = (18,12)
plt.figure(figsize=IMAGE_SIZE)
img_np=mpimg.imread(sample_image)
img = Image.fromarray(img_np.astype('uint8'),'RGB')
x, y = img.size

fig,ax = plt.subplots(1, figsize=(15,15))
# Display the image
ax.imshow(img_np)

# draw box and label for each detection
detections = json.loads(resp.text)
for detect in detections['boxes']:
    label = detect['label']
    box = detect['box']
    conf_score = detect['score']
    if conf_score > 0.6:
        ymin, xmin, ymax, xmax =  box['topY'],box['topX'], box['bottomY'],box['bottomX']
        topleft_x, topleft_y = x * xmin, y * ymin
        width, height = x * (xmax - xmin), y * (ymax - ymin)
        print('{}: [{}, {}, {}, {}], {}'.format(detect['label'], round(topleft_x, 3),
                                                round(topleft_y, 3), round(width, 3),
                                                round(height, 3), round(conf_score, 3)))

        color = np.random.rand(3) #'red'
        rect = patches.Rectangle((topleft_x, topleft_y), width, height,
                                 linewidth=3, edgecolor=color,facecolor='none')

        ax.add_patch(rect)
        plt.text(topleft_x, topleft_y - 10, label, color=color, fontsize=20)

plt.show()

Clean up resources

Do not complete this section if you plan on running other Azure Machine Learning tutorials.

If you don't plan to use the resources you created, delete them, so you don't incur any charges.

In the Azure portal, select Resource groups on the far left.
From the list, select the resource group you created.
Select Delete resource group.
Enter the resource group name. Then select Delete.

You can also keep the resource group but delete a single workspace. Display the workspace properties and select Delete.

Next steps

In this automated machine learning tutorial, you did the following tasks:

Configured a workspace and prepared data for an experiment.
Trained an automated object detection model
Specified hyperparameter values for your model
Performed a hyperparameter sweep
Deployed your model
Visualized detections

Learn more about computer vision in automated ML.
Learn how to set up AutoML to train computer vision models with Python.
Learn how to configure incremental training on computer vision models.
See what hyperparameters are available for computer vision tasks.
Review detailed code examples and use cases in the GitHub notebook repository for automated machine learning samples. Please check the folders with 'image-' prefix for samples specific to building computer vision models.

Note

Use of the fridge objects dataset is available through the license under the MIT License.

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