You can train image classification and object detection models using Vertex AI. Here’s a comprehensive overview of the process:

1. Data Preparation

• Supported Formats: Vertex AI supports common image formats like JPEG, PNG, and TIFF. The maximum file size per image is 30MB for training data and 1.5MB for prediction data.
• Data Quality: Ensure your training data is representative of the data you’ll use for predictions. Consider including variations in angle, resolution, and background.
• Labeling: You’ll need to label your images. For classification, this means assigning categories to each image. For object detection, you’ll need to draw bounding boxes around objects of interest and assign labels to them.
• Dataset Size: Google recommends about 1000 training images per label, with a minimum of 10.
• Data Split: Divide your data into training, validation, and test sets. Vertex AI allows you to control the split ratios.
• Storage: Store your images in Google Cloud Storage (GCS).

2. Training Options

Vertex AI offers two main approaches for image model training:

• AutoML: This option is suitable if you want to train a model with minimal code. Vertex AI handles the model selection, architecture, and hyperparameter tuning automatically.
  • How it works: You upload your labeled image data to Vertex AI Datasets, select AutoML as the training method, and configure training parameters like the training budget (node hours).
  • Model types: AutoML supports image classification (single-label and multi-label) and object detection.
• Custom Training: This option gives you more control and flexibility. You can use any ML framework (TensorFlow, PyTorch, etc.) and customize the training process.
  • How it works: You provide your own training script, which defines the model architecture, training loop, and any custom preprocessing or evaluation steps. You can package your training code into a Docker container.
  • Use cases: Custom training is ideal for complex models, specialized architectures, or when you need fine-grained control over the training process.

3. Training Steps

Here’s a general outline of the steps involved in training an image model on Vertex AI:

1. Create a Dataset: In the Vertex AI section of the Google Cloud Console, create a new Dataset and select the appropriate data type (e.g., “Images”).
2. Import Data: Import your labeled image data from Google Cloud Storage into the Dataset. You can use JSON Lines or CSV files to specify the image paths and labels.
3. Train a Model:
   • AutoML: Select “Train new model” from the Dataset page, choose “AutoML” as the training method, and configure the training job.
   • Custom Training: Create a custom training job, specify your training script or container, and configure the compute resources (machine type, accelerators, etc.).
4. Evaluate the Model: After training, Vertex AI provides tools to evaluate your model’s performance (e.g., confusion matrices, precision-recall curves).
5. Deploy the Model (Optional): If you want to serve predictions online, you can deploy your trained model to a Vertex AI Endpoint.
6. Get Predictions:
   • Online Predictions: Send individual image requests to your deployed endpoint and get real-time predictions.
   • Batch Predictions: Process a large batch of images and store the predictions in a BigQuery table or Cloud Storage.

4. Code Examples (Conceptual)

Here are some conceptual code snippets (using the Vertex AI SDK for Python) to illustrate the process:

AutoML Image Classification:

Python

from google.cloud import aiplatform

aiplatform.init(project="your-project-id", location="us-central1")

dataset = aiplatform.ImageDataset.create(
    display_name="my-image-dataset",
    gcs_source=["gs://your-bucket/data/image_classification_data.jsonl"],
)

model = aiplatform.AutoMLImageTrainingJob(
    display_name="my-image-classification-model",
    prediction_type="classification",
    multi_label=False,  # Set to True for multi-label classification
).run(
    dataset=dataset,
    model_display_name="my-trained-model",
    training_fraction_split=0.8,
    validation_fraction_split=0.1,
    test_fraction_split=0.1,
    # Add more parameters as needed
)

Custom Training (Simplified):

Python

from google.cloud import aiplatform

aiplatform.init(project="your-project-id", location="us-central1")

job = aiplatform.CustomContainerTrainingJob(
    display_name="my-custom-image-training-job",
    container_uri="us-docker.pkg.dev/your-project/your-container-registry/your-image:latest",  # Your Docker image
    model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-7:latest",  # Example for TensorFlow
)

model = job.run(
    dataset="your-dataset-resource-name",  # Or specify training data directly
    model_display_name="my-custom-trained-model",
    # Add more parameters as needed
)

5. Key Considerations

• Compute Resources: Choose appropriate machine types and accelerators (GPUs, TPUs) based on your model complexity and dataset size.
• Training Budget: For AutoML, set a training budget (node hours) to control costs.
• Model Evaluation: Carefully evaluate your model’s performance on the test set.
• Prediction: Choose the appropriate prediction method (online or batch) based on your application’s requirements.
• Vertex AI Feature Store: Consider using Feature Store to manage and serve features for your image models.

By following these guidelines and leveraging Vertex AI’s capabilities, you can efficiently train and deploy image models for various applications. Remember to consult the official Google Cloud documentation for the most up-to-date information and best practices.