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In healthcare, it aids in the interpretation of medical images, assisting clinicians in diagnosing diseases and identifying anomalies with greater precision. Read about some of our use cases here.

Similarly, in retail, image recognition streamlines checkout processes, and, together with visual search, enhances customer experience through personalized recommendations.

In security, it strengthens surveillance systems by enabling real-time monitoring, threat detection, and facial recognition.

This technology is also essential for autonomous vehicles, enabling them to perceive their surroundings through cameras and sensors, recognize objects, pedestrians, and road signs, and make real-time decisions for safe navigation.

Additionally, image recognition systems help in both research and applied sciences. For instance, in biological research, microscopy image analysis and wildlife conservation. It plays a crucial role in monitoring and protecting endangered species. It enables researchers and conservationists to analyze vast amounts of camera trap data efficiently, identifying and tracking individual animals, assessing population dynamics, and detecting potential threats such as poaching or habitat loss.

Image recognition aids satellite imagery analysis, especially in monitoring vegetation coverage crucial for sectors like insurance and agriculture. LAICA, by World From Space (WFS) and Ximilar, addresses this, using deep learning to merge satellite data for daily vegetation monitoring despite cloud cover challenges.

In social media, image recognition facilitates image tagging and content moderation.

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Additionally, image recognition helps with consumer research, enabling retailers to gain insights into customer demographics and behaviour within physical stores. This information aids in optimizing store layouts, product placements, and staffing strategies to enhance the overall shopping experience.

Image recognition also supports personalized marketing initiatives by analyzing customer preferences and purchase history, allowing e-shops to tailor promotions and recommendations accordingly. This personalized shopping experience fosters stronger customer engagement and increases sales.

In many of these applications, image recognition works in tandem with visual search technology, which identifies visually similar products to items detected in product photos and real-life images.

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Ximilar provides several off-the-shelf solutions for the detection, recognition, tagging and sorting of specific image data, such as stock photos, home decor and furniture images, fashion photos, or trading and collectible cards.

Anyone can create and train their own custom image recognition models on Ximilar’s computer vision platform. Namely, you can train:

• image classification models: categorization & tagging, image regression (value prediction)
• object detection models

It requires no coding experience. The custom models can be easily combined with existing ready-to-use solutions with Flows.

Object Detection requires manual annotation of training data, which can be done both in our App and dedicated interface Annotate.

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