Platform – Overview & Basics

This page describes the Ximilar Computer Vision Platform for training custom image recognition models. It supports model types like classification, regression, tagging, object detection, and their combinations via Flows. These models leverage visual AI and machine learning architectures such as convolutional neural networks (CNNs) and vision transformers.

The Ximilar App provides a simple interface for setting up your plan and tasks, uploading images, training models, and evaluating the results. Trained models are automatically deployed as API endpoints for easy integration into your application or system.

Unlike large language models, these computer vision models are optimized for speed and cost, and can even run on edge devices.

Terminology

A task is the starting point for your machine learning project. A task represents the abstract definition for training a recognition model. Each task has a set of labels. Each label can be assigned to multiple training images. Your tasks, data, and images are private and accessible only to you.

A model is the trained version of a task – essentially a neural network trained on your specific images. This makes it highly accurate at recognizing your new images. Each model has an accuracy metric measured at the end of training. Models are private to their owner. Each time you retrain a model, a new version is created with an incremented version number. You can select which model version to deploy in production.

Tasks can be of different types:

• Image Classification tasks, further divided into:
  • Tagging ('multi_label'): Assigns multiple labels to an image.
  • Categorization ('multi_class'): Assigns a single category to an image.
  • Regression ('regression'): Predicts a numerical value (e.g., an age) from an image.
• Detection tasks: Identifies and locates objects within images.
• Similarity tasks: Determines how similar images are to each other.

A label represents a feature you want to recognize in your images. Each task has one or several labels. For image classification tasks, labels have specific types depending on the use case:

• category for categorization tasks (e.g., shoes)
• tag for tagging tasks (e.g., leather, brown, high heels)
• value for regression tasks (e.g., age, height, weight)

To train a model, you must upload training images and either assign labels to them or define objects within them (annotate).

A workspace is where all your images, labels, and tasks are stored. By default, each user owns at least one workspace. You can share your workspace and grant access to other users.

Multiple tasks of different types can be combined and called together via the Flows service.

For example, to recognize apples and bananas in images, you can create a categorization task with two labels: 'apple' and 'banana'. For each label, you need to upload at least 20 images. Once the images are uploaded, you can train the task — the training process may take several minutes to a few hours, depending on the dataset and settings. The result of this training is a model, which is deployed as an API. You can then send simple REST requests with images to classify them as either apple or banana.

Task Types

The Ximilar Computer Vision Platform allows you to train following types of tasks (via App or API):

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What's next?

First check out our Quickstart guide to get started with the platform. Then try to query details about your user account, statistics, workspaces, and available solutions – User Management. After that, you can start uploading images, creating labels, and training your first model – Image Classification or Object Detection. If you need to combine the models, you can use Flows.