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Label and Train a Custom Model

By Ashwin Venugopal -
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  Introduction Labeling your photos and connecting the resulting COCO file are the following steps after creating your dataset and uploading your photos to blob storage. You can omit the labeling step if your training photos already contain a COCO file. Labeling Your Training Images You can use the Data Labeling Project in Azure Machine Learning Studio to label your training photos. The performance of your trained model is significantly enhanced by having correct and comprehensive labels for your training images. Make sure to fully label every instance of each class when labeling your photographs. Make a new Azure Machine Learning Data Labeling project in your dataset in Vision Studio, or link to an already-existing project if you made one in Azure Machine Learning Studio. After creating your project, clicking that button will launch the labeling project in Azure Machine Learning Studio. You can add categories (such apple, orange, and banana) to your pictures or objects in Azure Ma...
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Create a Custom Project

By Ashwin Venugopal -
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  Introduction To develop a personalized Azure AI Vision model, you initially require an Azure AI Services resource (or an Azure AI Vision resource). After deploying that resource to your subscription, the next step is to establish a custom project. Components Of a Custom Vision Project The initial element of a tailored project is the dataset. This dataset comprises your assortment of images for training your model, along with the COCO file that outlines the labeling details associated with those images. After defining your images and class labels, you can begin training your custom model. During the training process, you'll indicate the model type to be trained, the dataset to utilize, and your allocated training time budget. Once the model training has finished, you can assess its performance and utilize the model for making predictions. In most cases, the steps you follow are: Create your blob storage container and upload just the training images. Create the dataset for your pro...
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Image Classification With Custom Azure AI Vision Models

By Ashwin Venugopal -
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  Introduction The field of artificial intelligence that deals with visual perception is called computer vision. Several services that enable typical computer vision scenarios are included in Azure AI Vision. You may train an AI model to identify objects in photographs or classify images using custom models in Azure AI Vision. In order to categorize (or classify) an image, software must examine it. This is a frequent computer vision challenge. Another prevalent computer vision issue is object detection, which calls for software to locate particular object classes inside an image. From development to labeling and training, the process of creating an object detection project is similar to that of creating an image classification project. Understand Custom Model Types Custom Azure AI Vision models have different functionality based on the type. The types of custom models include Image classification, Object detection, and Product recognition. Image Classification A computer vision fea...
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Analyze Images (Part 2)

By Ashwin Venugopal -
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  Generate a Smart-Cropped Thumbnail and Remove Background Applications and websites frequently use thumbnails to display smaller representations of photos. A tourism website might, for instance, list all of the city's tourist attractions with a brief, representative thumbnail image for each one. The full image would only be seen when the visitor chooses the details page for a particular attraction. You can use the Azure AI Vision service to create a thumbnail with different dimensions (and aspect ratio) from the original image. You can also choose to utilize image analysis to identify the image's main subject, or region of interest, and make that the thumbnail's focal point. When cropping an image to alter its aspect ratio, this ability to identify the region of interest is very helpful. You can specify the aspect ratio of the cropped image (width / height), ranging from 0.75 to 1.80 . Remove Image Background The image can be divided into the main subject and all backgroun...
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Analyze Images (Part 1)

By Ashwin Venugopal -
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  Introduction The field of artificial intelligence that deals with visual perception is called computer vision. Several services that enable typical computer vision scenarios are included in Azure AI Vision. A subfield of artificial intelligence (AI) called Azure AI Vision uses software to understand visual data, frequently from photos or video feeds. Provision an Azure AI Vision Resource The Azure AI Vision service is designed to help you extract information from images. It provides functionality that you can use for: Description and tag generation - determining an appropriate caption for an image, and identifying relevant "tags" that can be used as keywords to indicate its subject. Object detection - detecting the presence and location of specific objects within the image. People detection - detecting the presence, location, and features of people in the image. Image metadata, color, and type analysis - determining the format and size of an image, its dominant color pa...
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Azure AI Content Safety

By Ashwin Venugopal -
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  When to use Azure AI Content Safety? Numerous online platforms motivate users to express their opinions. Individuals have faith in the reviews of other users regarding products, services, brands, and beyond. Such feedback is frequently candid, perceptive, and perceived as being devoid of promotional bias. However, not every piece of content is created with good intentions. Azure AI Content Safety is an artificial intelligence service aimed at delivering a more thorough method of content moderation. Azure AI Content Safety helps organizations to prioritize work for human moderators in a growing number of situations: Education The quantity of learning platforms and online educational resources is increasing quickly, with a constant influx of new information. Educators must ensure that students are not encountering inappropriate material or submitting harmful queries to LLMs. Furthermore, both educators and students desire assurance that the information they are engaging with is acc...
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Azure AI Content Safety

By Ashwin Venugopal -
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  How Does Azure AI Content Safety Work? Azure AI Content Safety is designed to work with both text and images, as well as content generated by AI. It can identify and moderate inappropriate material. The visual capabilities of Content Safety are driven by Microsoft's Florence foundation model, which has been trained on billions of pairs of text and images. The analysis of text employs natural language processing methods to enhance the understanding of subtlety and context. Azure AI Content Safety supports multiple languages and is capable of recognizing harmful content in both short and long formats. It is currently available in English, German, Spanish, French, Portuguese, Italian, and Chinese. Azure AI Content Safety features include: Safeguarding Text Content Moderate text scans text across four categories: violence, hate speech, sexual content, and self-harm. A severity level from 0 to 6 is returned for each category. This level helps to prioritize what needs immediate attent...
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