Challenge 25: Custom Vision - Image Classification

Estimated Time

60 min | Cost: $2-5 (estimated) | Domain: Computer Vision Solutions (10-15%)

Exam skills covered

• Choose between image classification and object detection
• Label images for training custom models
• Train a custom image model
• Evaluate model metrics (precision, recall, AP)
• Publish and consume custom model iterations

Overview

Custom Vision enables training domain-specific image classification models without ML expertise. Two project types:

Type	Use Case	Output
Classification - Multiclass	Image belongs to ONE category	Single tag per image
Classification - Multilabel	Image can have MULTIPLE categories	Multiple tags per image
Object Detection	Locate objects with bounding boxes	Tags + coordinates

Training produces iterations with metrics: Precision (of predicted positives, how many are correct), Recall (of actual positives, how many were found), and AP (Average Precision — area under precision-recall curve).

Prerequisites

• Azure subscription
• Custom Vision Training + Prediction resources
• Python 3.9+
• Packages: azure-cognitiveservices-vision-customvision (training + prediction)

Implementation

Task 1: Create Custom Vision Resources

az group create --name rg-ai102-customvision --location eastus2

# Training resource
az cognitiveservices account create \
  --name cv-training-ai102 \
  --resource-group rg-ai102-customvision \
  --kind CustomVision.Training \
  --sku S0 \
  --location eastus2

# Prediction resource
az cognitiveservices account create \
  --name cv-prediction-ai102 \
  --resource-group rg-ai102-customvision \
  --kind CustomVision.Prediction \
  --sku S0 \
  --location eastus2

# Get keys
TRAINING_KEY=$(az cognitiveservices account keys list --name cv-training-ai102 --resource-group rg-ai102-customvision --query key1 -o tsv)
TRAINING_ENDPOINT=$(az cognitiveservices account show --name cv-training-ai102 --resource-group rg-ai102-customvision --query properties.endpoint -o tsv)
PREDICTION_KEY=$(az cognitiveservices account keys list --name cv-prediction-ai102 --resource-group rg-ai102-customvision --query key1 -o tsv)
PREDICTION_ENDPOINT=$(az cognitiveservices account show --name cv-prediction-ai102 --resource-group rg-ai102-customvision --query properties.endpoint -o tsv)

Task 2: Create Project, Upload and Tag Images, Train

Python SDK

import os
import time
from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
from azure.cognitiveservices.vision.customvision.training.models import ImageUrlCreateEntry
from msrest.authentication import ApiKeyCredentials

training_endpoint = os.environ["CUSTOM_VISION_TRAINING_ENDPOINT"]
training_key = os.environ["CUSTOM_VISION_TRAINING_KEY"]
prediction_endpoint = os.environ["CUSTOM_VISION_PREDICTION_ENDPOINT"]
prediction_key = os.environ["CUSTOM_VISION_PREDICTION_KEY"]

# Create training client
training_credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
trainer = CustomVisionTrainingClient(training_endpoint, training_credentials)

# Create a classification project (Multiclass)
project = trainer.create_project(
    name="Fruit-Classifier",
    classification_type="Multiclass",
    domain_id=None  # Use General domain
)
print(f"Created project: {project.id}")

# Create tags (categories)
apple_tag = trainer.create_tag(project.id, "apple")
banana_tag = trainer.create_tag(project.id, "banana")
orange_tag = trainer.create_tag(project.id, "orange")
print(f"Tags created: apple={apple_tag.id}, banana={banana_tag.id}, orange={orange_tag.id}")

# Upload training images (using URLs)
apple_images = [
    "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/e/ee/Apples.jpg",
]
banana_images = [
    "https://upload.wikimedia.org/wikipedia/commons/8/8a/Banana-Single.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/d/de/Bananacomp.jpg",
]

# Upload apple images
image_entries = [ImageUrlCreateEntry(url=url, tag_ids=[apple_tag.id]) for url in apple_images]
upload_result = trainer.create_images_from_urls(project.id, images=image_entries)
print(f"Apple images uploaded: {upload_result.is_batch_successful}")

# Upload banana images
image_entries = [ImageUrlCreateEntry(url=url, tag_ids=[banana_tag.id]) for url in banana_images]
upload_result = trainer.create_images_from_urls(project.id, images=image_entries)
print(f"Banana images uploaded: {upload_result.is_batch_successful}")

# Train the model (requires minimum 5 images per tag for best results)
print("\nTraining model...")
iteration = trainer.train_project(project.id)

while iteration.status != "Completed":
    iteration = trainer.get_iteration(project.id, iteration.id)
    print(f"  Status: {iteration.status}")
    time.sleep(5)

print(f"Training complete! Iteration: {iteration.id}")

# Get performance metrics
performance = trainer.get_iteration_performance(project.id, iteration.id)
print(f"\nPerformance Metrics:")
print(f"  Precision: {performance.precision:.4f}")
print(f"  Recall: {performance.recall:.4f}")
print(f"  AP (Average Precision): {performance.average_precision:.4f}")

for tag_perf in performance.per_tag_performance:
    print(f"  Tag '{tag_perf.name}': precision={tag_perf.precision:.3f}, recall={tag_perf.recall:.3f}")

Task 3: Publish and Make Predictions

Python SDK

# Publish the iteration for prediction
prediction_resource_id = f"/subscriptions/<sub-id>/resourceGroups/rg-ai102-customvision/providers/Microsoft.CognitiveServices/accounts/cv-prediction-ai102"
publish_name = "fruit-model-v1"

trainer.publish_iteration(
    project.id,
    iteration.id,
    publish_name,
    prediction_resource_id
)
print(f"Model published as: {publish_name}")

# Create prediction client
prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
predictor = CustomVisionPredictionClient(prediction_endpoint, prediction_credentials)

# Make a prediction with URL
test_url = "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"

results = predictor.classify_image_url(
    project.id,
    publish_name,
    url=test_url
)

print(f"\nPrediction results:")
for prediction in results.predictions:
    print(f"  {prediction.tag_name}: {prediction.probability:.4f} ({prediction.probability*100:.1f}%)")

# Prediction with local file
with open("test-fruit.jpg", "rb") as image_file:
    results = predictor.classify_image(project.id, publish_name, image_file)
    for prediction in results.predictions:
        if prediction.probability > 0.5:
            print(f"  Classified as: {prediction.tag_name} ({prediction.probability:.2%})")

REST API

PREDICTION_ENDPOINT="https://<resource>.cognitiveservices.azure.com"
PREDICTION_KEY="<key>"
PROJECT_ID="<project-id>"
PUBLISH_NAME="fruit-model-v1"

# Classify image by URL
curl -s "${PREDICTION_ENDPOINT}/customvision/v3.0/prediction/${PROJECT_ID}/classify/iterations/${PUBLISH_NAME}/url" \
  -H "Prediction-Key: ${PREDICTION_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"
  }' | jq '.predictions[] | {tag: .tagName, probability: .probability}'

Expected Output

Created project: a1b2c3d4-e5f6-7890-abcd-ef1234567890
Tags created: apple=..., banana=..., orange=...
Apple images uploaded: True
Banana images uploaded: True

Training model...
  Status: Training
  Status: Training
  Status: Completed
Training complete! Iteration: iter-12345

Performance Metrics:
  Precision: 0.9200
  Recall: 0.8800
  AP (Average Precision): 0.9450
  Tag 'apple': precision=0.950, recall=0.900
  Tag 'banana': precision=0.890, recall=0.860

Model published as: fruit-model-v1

Prediction results:
  apple: 0.9723 (97.2%)
  banana: 0.0198 (2.0%)
  orange: 0.0079 (0.8%)

Break & fix

Scenario	Symptom	Root Cause	Fix
Training fails	BadRequestImageCount	Less than 5 images per tag	Add at least 5 images per category
Low precision	Many false positives	Training images too similar across tags	Add diverse negative examples; use more distinct images
Publish fails	Resource ID error	Wrong prediction resource ID format	Use full ARM resource ID path
Prediction returns 404	Iteration not found	Model not published or wrong publish name	Verify publish_iteration succeeded; use correct name
Image upload fails	ImageUrl not reachable	URL not accessible from Azure	Use publicly accessible URLs or upload file directly

Knowledge Check

1. What is the difference between Multiclass and Multilabel classification?

2. What does Average Precision (AP) measure in Custom Vision?

3. What is required before making predictions with a trained Custom Vision model?

4. What is the minimum number of training images recommended per tag?

5. How do you consume a published Custom Vision model for predictions?

Cleanup

az group delete --name rg-ai102-customvision --yes --no-wait

Learn More

• Custom Vision overview
• Quickstart: Build a classifier
• Evaluate and improve model