Azure AI Vision

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Azure AI Vision and other Azure AI Services offerings guarantee 99.9 percent availability. No SLA is provided for the Free pricing tier. See SLA details.

No. Microsoft automatically deletes your images and videos after processing and does not train on your data to enhance the underlying models. Video data does not leave your premises, and video data is not stored on the edge where the container runs. Learn more about privacy and terms of usage.

No, spatial analysis detects and locates human presence in video footage and outputs a bounding box around each person detected. The AI models do not detect faces nor determine individuals’ identities nor demographics.

The spatial analysis AI models detect and track movements in the video feed based on algorithms that identify the presence of one or more humans by a body bounding box. For each person and bounding box detected in a zone in the camera field of view, the AI models output event data including bounding box coordinates of a person’s body, event type (for example, zone entry or exit, or directional line crossing), pseudonymous identifiers to track the bounding box, and a detection confidence score. This event data is sent to your own instance of Azure IoT Hub.

Yes. Because model customization is designed to be fine-tuned for your scenario, you need to provide the labeled data to train your model.

The model customization feature of the service is optimized to quickly recognize major differences between images, so you can start prototyping your model with a small amount of data. You may start with as little as one image per label. If you have more labeled images, you may add more. Depending on the complexity of the problem and degree of accuracy required, you can continue adding additional images per label to improve your model.

It’s both. You can use the site to access a graphical interface for managing datasets, training, and evaluation of models for a no-code experience, or, as an alternative, you can use the Computer Vision APIs.

You can label the images in Azure Machine Learning Studio, which is integrated with Vision Studio for easy export of labeled data. You can also label the data in the COCO file format and import the COCO file directly in Vision Studio. See documentation for details.

The model customization feature for Azure AI Vision is the next generation of Custom Vision, with improved accuracy and few-shot learning capabilities. You may continue to use Custom Vision, or you can migrate your training data to retrain your model with model customization from Azure AI Vision. See documentation for details.

After using Azure AI Vision to extract insights and text from images and video, you can use text analytics to analyze sentiment, Translator to translate text into your desired language, or Immersive Reader to read the text aloud, making it more accessible. Related services and capabilities include Azure AI Document Intelligence to extract key-value pairs and tables from documents, Azure AI Video Indexer for extracting advanced metadata from audio and video files, and Content Moderator to detect unwanted text or images.