Training Custom Models

Documentation Index

Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt

Use this file to discover all available pages before exploring further.

Train custom image classification and object detection models using Azure Custom Vision Studio: a visual, low-code interface for building, training, and publishing models that detect or classify objects in images. Start with labeled images (for example, apples, oranges, and bananas) and let Custom Vision do the heavy lifting of model training, evaluation, and publishing. Explore Custom Vision Studio: https://learn.microsoft.com/azure/cognitive-services/custom-vision-service/ Once your images are uploaded and tagged, use the Train button (top of the project page) to start training. The UI guides you from upload → labeling → training → evaluation, making iteration straightforward.

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End-to-end workflow (high level)

Benefits of using Custom Vision Studio:

• Visual, user-friendly tooling for labeling and training (no deep ML expertise required).
• Domain-specific optimizations (Food, Retail, Landmarks, etc.) that can influence model architecture.
• Iterative improvement: add more labeled images and retrain to increase accuracy.

Next, walk through creating the Azure resource and the Custom Vision project.

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Create a Custom Vision resource in Azure

Create a Custom Vision resource in the Azure portal. You can enable both training and prediction on the same resource or separate them into training and prediction resources depending on your security and billing needs. Pick a subscription, resource group, region, and pricing tier (there is a free tier for testing).

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Create a project in the Custom Vision web app

Open the Custom Vision web app (https://www.customvision.ai/) and create a new project. Provide a name and optional description, select the linked resource, then choose:

• Project type:
  • Classification — whole-image labels (Multiclass: one tag per image; Multilabel: multiple tags per image).
  • Object detection — localize objects with bounding boxes and return tag predictions.
• Domain — e.g., General, Food, Retail, Landmarks. Domains may optimize architecture or export options.

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Upload and label images

• For classification: upload images and assign a single (or multiple for multilabel) tag per image.
• For object detection: upload images, create tags, then draw tight bounding boxes around each object instance and assign the correct tag.

After tagging, click Train. The platform produces iterations you can evaluate and compare.

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Evaluation metrics (what they mean)

After training, use Quick Test or the Predictions API to test on images held out from training.

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Testing with images stored in Azure Storage

You can test images stored in Azure Blob Storage by copying the blob URL (make it public or use a SAS token) and pasting it into Quick Test or sending it via the Predictions API. Quick Test displays predicted tags and probabilities for classification models. For example, a golden retriever image might return “Golden Retriever — 99.7%.” Validate generalization by testing with diverse images, including web images and held-out datasets.

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Object detection: annotate, train, and evaluate

Object detection projects return both labels and bounding boxes. Workflow:

1. Create an object detection project (e.g., DogDetector).
2. Upload images.
3. Create tags (e.g., Golden Retriever, Husky).
4. Select images, draw bounding boxes for each instance, and assign tags.
5. Train and review metrics (Precision, Recall, mAP).

After annotating, train the model and inspect per-tag performance. Use Quick Test to verify bounding box outputs, confidence scores, and multiple detections per image. If detection quality is insufficient:

• Add images that increase variation (lighting, scale, occlusion, orientation).
• Correct and tighten bounding boxes.
• Increase labeled instances per tag and retrain.

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Publish for production

When satisfied with an iteration, publish it to create a prediction endpoint and obtain keys/credentials. Use the SDK or REST Prediction API to integrate the model into web, mobile, or backend systems. API & SDK references:

• Predictions API: https://learn.microsoft.com/azure/cognitive-services/custom-vision-service/
• Custom Vision documentation: https://learn.microsoft.com/azure/cognitive-services/custom-vision-service/

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Summary

• Use Custom Vision Studio to create classification or object detection projects, upload and tag images, train iterations, evaluate precision/recall/mAP, and test with Quick Test.
• Publish trained iterations to obtain prediction endpoints for programmatic integration.
• Improve models iteratively—collect diverse labeled data, correct annotations, and retrain to improve real-world performance.