Concepts
Training custom image models, including image classification and object detection, is a crucial aspect of designing and implementing a Microsoft Azure AI solution. In this article, we will explore the process of training custom image models using Azure’s AI services.
1. Image Classification
Image classification is the task of assigning a label or category to an image. Azure Cognitive Services provides a pre-trained image classification model called the Custom Vision service. However, if the pre-trained model doesn’t suit your needs, you can create your own custom image classification model using Azure Machine Learning.
To train a custom image classification model in Azure Machine Learning, you need a labeled dataset containing images along with their corresponding labels. The dataset should be divided into training and validation sets. Azure Machine Learning supports popular deep learning frameworks such as TensorFlow and PyTorch for training custom image classification models.
Here is an example of how you can train a custom image classification model using Azure Machine Learning with TensorFlow:
import azureml.core
from azureml.core import Workspace, Dataset
from azureml.core.compute import ComputeTarget
from azureml.core.compute_target import ComputeTargetException
from azureml.train.dnn import TensorFlow
# Connect to your Azure Machine Learning workspace
ws = Workspace.from_config()
# Specify the training dataset
dataset = Dataset.get_by_name(ws, name='training_dataset')
# Define GPU compute target for training
compute_target = ComputeTarget(ws, 'gpu-cluster')
# Define the training script
script_params = {
'--data-folder': dataset.as_mount(),
'--batch-size': 32,
'--epochs': 10,
'--learning-rate': 0.001
}
estimator = TensorFlow(source_directory='./scripts',
compute_target=compute_target,
entry_script='train.py',
script_params=script_params,
framework_version='2.0',
conda_packages=['numpy', 'tensorflow-gpu'])
# Submit the training job
run = experiment.submit(estimator)
# Monitor the training progress
run.wait_for_completion(show_output=True)
2. Object Detection
Object detection involves identifying and localizing multiple objects within an image. Azure Cognitive Services offers a pre-trained object detection model called the Custom Vision service. However, if you need to customize the object detection model further, you can use Azure Machine Learning.
To train a custom object detection model using Azure Machine Learning, you need a labeled dataset with annotations specifying object bounding boxes. Azure Machine Learning supports frameworks like TensorFlow and PyTorch, along with popular object detection libraries such as Detectron2 and YOLO.
Here is an example of how you can train a custom object detection model using Azure Machine Learning with Detectron2:
import azureml.core
from azureml.core import Workspace, Dataset
from azureml.core.compute import ComputeTarget
from azureml.core.compute_target import ComputeTargetException
from azureml.train.dnn import PyTorch
# Connect to your Azure Machine Learning workspace
ws = Workspace.from_config()
# Specify the training dataset
dataset = Dataset.get_by_name(ws, name='training_dataset')
# Define GPU compute target for training
compute_target = ComputeTarget(ws, 'gpu-cluster')
# Define the training script
script_params = {
'--data-folder': dataset.as_mount(),
'--batch-size': 2,
'--epochs': 10,
'--learning-rate': 0.001
}
estimator = PyTorch(source_directory='./scripts',
compute_target=compute_target,
entry_script='train.py',
script_params=script_params,
conda_packages=['numpy', 'torch', 'torchvision', 'detectron2'])
# Submit the training job
run = experiment.submit(estimator)
# Monitor the training progress
run.wait_for_completion(show_output=True)
In both examples, you need to define your Azure Machine Learning workspace and specify the training dataset. Additionally, you need to set up a compute target, which can be a GPU cluster for faster training. The training script should be defined with appropriate parameters and dependencies.
Once the training job is submitted, you can monitor the progress and evaluate the model’s performance. Azure Machine Learning provides tools to monitor metrics, view log files, and visualize the training process.
In conclusion, training custom image models for tasks like image classification and object detection is made easy with Azure’s AI services. Whether you choose Azure Cognitive Services or Azure Machine Learning, you have the flexibility and power to train models that meet your specific requirements. Start utilizing these services today to enhance your AI solutions with custom image models.
Answer the Questions in Comment Section
True/False: In object detection, the goal is to identify and classify objects within an image.
Answer: True
Multiple Select: Which of the following are common techniques for image classification?
- a) Convolutional Neural Networks (CNN)
- b) Support Vector Machines (SVM)
- c) Principal Component Analysis (PCA)
- d) Random Forests (RF)
Answer: a) Convolutional Neural Networks (CNN), b) Support Vector Machines (SVM)
Single Select: Which Azure service can be used to train custom image classification models?
- a) Azure Machine Learning
- b) Azure Cognitive Services
- c) Azure Computer Vision
Answer: a) Azure Machine Learning
Multiple Select: What are the benefits of using transfer learning for image classification?
- a) It allows for faster training times
- b) It requires a smaller labeled dataset
- c) It improves model accuracy
- d) It eliminates the need for pre-processing images
Answer: a) It allows for faster training times, b) It requires a smaller labeled dataset, c) It improves model accuracy
True/False: In object detection, bounding boxes are used to locate and outline objects within an image.
Answer: True
Multiple Select: Which of the following techniques can be used for object detection?
- a) Faster R-CNN
- b) YOLO (You Only Look Once)
- c) K-means clustering
- d) Mask R-CNN
Answer: a) Faster R-CNN, b) YOLO (You Only Look Once), d) Mask R-CNN
Single Select: Which Azure service provides pre-trained models for image classification and object detection?
- a) Azure Machine Learning
- b) Azure Cognitive Services
- c) Azure Computer Vision
Answer: b) Azure Cognitive Services
True/False: Data labeling is an important step in training custom image models as it helps the model understand and learn from the provided data.
Answer: True
Single Select: Which Azure service can be used to deploy custom image classification models as a RESTful API?
- a) Azure Machine Learning
- b) Azure Cognitive Services
- c) Azure Computer Vision
Answer: a) Azure Machine Learning
Multiple Select: Which of the following can be considered best practices for training custom image models?
- a) Using a diverse and representative dataset
- b) Augmenting the dataset with additional synthetic images
- c) Fine-tuning models with transfer learning
- d) Increasing the complexity of the model architecture
Answer: a) Using a diverse and representative dataset, c) Fine-tuning models with transfer learning
Excellent blog post on training custom image models!
How effective is the Azure Custom Vision Service for image classification?
Thanks for the information!
Does someone have experience with object detection models on Azure AI?
Great read! This will help me prepare for my AI-102 exam.
I appreciate the blog post, very informative!
The detailed steps on image classification are very useful.
One thing to note is the limitation on the training images. Start with a small set for quick iterations.