Tutorial / Cram Notes
Custom Image Classification
Custom Vision enables you to categorize images into a set of defined tags or categories. After you upload and tag a set of images, Custom Vision trains a model that can classify new images according to those tags.
- Robust Set of Supported Image Types: The service supports a variety of image formats, including JPEG, PNG, BMP, and GIF.
- Easy to Use: No machine learning expertise is required to train a model.
- Tagging: Train your model by tagging images with labels corresponding to the categories you need the model to recognize.
- Batch Prediction: Classify multiple images in one transaction.
Custom Object Detection
In addition to classifying images, Custom Vision can detect and locate objects within images. This allows for models to understand pictures at a more granular level by identifying the presence of objects and their coordinates within the image.
- Bounding Boxes: Define and tag objects within images with bounding boxes to train an object detection model.
- Multi-object Detection: Detect multiple objects within the same image.
Model Training and Improvement
Once you’ve uploaded and tagged a suitable set of images, you can train your model. Custom Vision uses this training set to understand the visual features associated with each tag.
- Incremental Training: As you tag and upload new images, you can retrain your model to improve its accuracy.
- Advanced Training Capabilities: Custom models can be trained to recognize hard-to-distinguish objects through advanced training algorithms.
Model Export and Integration
One significant advantage of Custom Vision is the flexibility it provides once you have a trained model. Models can be exported to run offline or integrated into applications.
- Multiple Export Formats: You can export models to various formats like TensorFlow, CoreML, ONNX, and Dockerfile.
- Edge Deployment: Exported models can be run on edge devices for real-time analysis.
Performance Monitoring and Analytics
Performance monitoring tools are embedded in Custom Vision to help users understand their model’s effectiveness and take steps to improve it as needed.
- Prediction Accuracy: Assess model performance through precision and recall metrics.
- Confusion Matrix: Visualize the performance of a classification model.
Examples of Custom Vision Applications
To give you an idea of the practical application of Custom Vision, here are a couple of examples where it can be used:
- Retail: Recognize and categorize products on shelves for inventory management.
- Agriculture: Detect diseased crops by analyzing aerial images of fields.
- Manufacturing: Locate defects in production lines by inspecting parts or assemblies.
- Security: Analyze surveillance footage to identify specific objects or people.
Conclusion
Custom Vision combines ease of use with robust analytics and export capabilities, enabling a wide range of users to create and deploy custom image recognition models. Whether for straightforward classification tasks or complex object detection, it stands as a powerful tool for anyone looking to utilize image-based AI without the need for deep expertise in machine learning algorithms.
By leveraging Custom Vision’s capabilities, businesses and developers can create solutions that automate and enhance visual recognition tasks, which can lead to efficiency improvements, cost savings, and the development of innovative new services and features.
Practice Test with Explanation
True or False: The Custom Vision Service requires extensive machine learning expertise to build, deploy, and improve a model.
- (1) True
- (2) False
Answer: False
Explanation: Custom Vision is designed to be an easy-to-use service that does not require extensive machine learning expertise. It provides a user-friendly interface and pre-built models that can be customized with your own data.
Does the Custom Vision Service allow for object detection in images?
- (1) Yes
- (2) No
Answer: Yes
Explanation: The Custom Vision Service provides capabilities for not only image classification but also for object detection, allowing users to identify and locate various objects within images.
Can the Custom Vision service be used to classify images with multiple tags?
- (1) Yes
- (2) No
Answer: Yes
Explanation: Custom Vision supports multi-label classification, enabling users to assign multiple tags to a single image when the image contains multiple objects of interest.
True or False: Models trained with Custom Vision Service can only be deployed on Azure.
- (1) True
- (2) False
Answer: False
Explanation: While Custom Vision Service is part of Azure, models trained with it can be exported and deployed elsewhere, including on iOS and Android devices, or to edge devices via containers.
Which of the following options are supported export formats for models trained with Custom Vision?
- (1) TensorFlow
- (2) ONNX
- (3) CoreML
- (4) All of the above
Answer: All of the above
Explanation: Custom Vision Service supports exporting models to several formats including TensorFlow, ONNX, and CoreML, allowing for flexibility in deployment.
What kind of algorithms does the Custom Vision service primarily utilize?
- (1) Recurrent neural networks
- (2) Convolutional neural networks
- (3) Support vector machines
- (4) Decision trees
Answer: Convolutional neural networks
Explanation: The Custom Vision service primarily uses convolutional neural networks (CNNs), which are highly effective for image recognition and classification tasks.
True or False: The Custom Vision service supports the capability to retrain models with new data.
- (1) True
- (2) False
Answer: True
Explanation: Custom Vision Service allows for continuous learning by enabling users to retrain models with new data to improve accuracy and adapt to new cases.
How can performance metrics like precision and recall be obtained for models trained with the Custom Vision Service?
- (1) Through Azure Machine Learning Service
- (2) Through Azure Cognitive Services
- (3) Using the Quick Test feature in Custom Vision
- (4) Directly in the Custom Vision portal after testing with tagged images
Answer: Directly in the Custom Vision portal after testing with tagged images
Explanation: The Custom Vision portal offers direct access to performance metrics like precision and recall after testing trained models with tagged images.
Can Custom Vision Service automatically generate tags for your images during training?
- (1) Yes
- (2) No
Answer: No
Explanation: While Custom Vision can suggest tags based on similar images, it requires users to manually tag their images for training, ensuring accurate and relevant tags are used to build the model.
True or False: Custom Vision Service can only process images stored in Azure Blob storage.
- (1) True
- (2) False
Answer: False
Explanation: Custom Vision Service can process images from various sources, not limited to Azure Blob storage. Users can upload images directly or use images from URLs.
Does the Custom Vision Service allow integration with other Azure services for enhanced workflows?
- (1) Yes
- (2) No
Answer: Yes
Explanation: Custom Vision can be integrated with other Azure services like Azure Functions and Logic Apps to create enhanced, automated workflows.
Interview Questions
1. Which of the following capabilities are provided by the Custom Vision service in Microsoft Azure? (Select all that apply.)
- a) Image classification
- b) Object detection
- c) Natural language understanding
- d) Speech recognition
Correct answer: a) Image classification, b) Object detection
2. Custom Vision service uses machine learning algorithms to automatically recognize and classify images. (True/False)
Correct answer: True
3. What is the maximum number of tags that can be associated with an image in the Custom Vision service?
- a) 10
- b) 50
- c) 100
- d) Unlimited
Correct answer: d) Unlimited
4. Custom Vision service provides a REST API that allows you to integrate the service with your applications. (True/False)
Correct answer: True
5. The Custom Vision service allows you to train a model using your own labeled images. (True/False)
Correct answer: True
6. Which of the following export formats are supported by the Custom Vision service? (Select all that apply.)
- a) TensorFlow
- b) ONNX
- c) PyTorch
- d) Spark MLlib
Correct answer: a) TensorFlow, b) ONNX
7. Custom Vision service can automatically generate code for prediction in various programming languages. (True/False)
Correct answer: True
8. The Custom Vision service provides a custom image recognition model that can be deployed on edge devices such as camera systems. (True/False)
Correct answer: True
9. Custom Vision service provides a pre-built model for recognizing handwritten digits. (True/False)
Correct answer: False
10. Which of the following can be used as a data source when training a model in Custom Vision service? (Select all that apply.)
- a) Local files on your machine
- b) Azure Blob storage
- c) Azure Cosmos DB
- d) Azure SQL Database
Correct answer: a) Local files on your machine, b) Azure Blob storage, d) Azure SQL Database
Great blog post on Custom Vision capabilities. Very detailed!
Can anyone explain how Custom Vision handles image classification?
Appreciate the post!
Does Custom Vision support object detection?
Thanks for the informative article.
Anyone know if Custom Vision can be integrated with other Azure services?
Very helpful, thanks!
Custom Vision’s ability to export models is a game-changer for edge computing.