Content-based image and video search for ecommerce. Search media assets with text queries, or retrieve similar images and videos based on visual content. Improve product discovery and customer experience by enabling intuitive search capabilities.
Based on multimodal embeddings for semantic content understanding, provides advanced visual search capabilities to find products quickly and easily using images or descriptive text.
Detect and track individuals in queues analyzing video feeds from cameras in real-time. Count the number of people in line, monitor waiting times, and even identify patterns in customer behavior to feed your analytics.
Manage queues in real-time and avoid common frustrations in retail stores. Act before bottlenecks form managing occupancy and queue lengths dynamically.
Understand customers behavior for improving their experience and business performance. Object tracking makes it possible for retailers to monitor foot traffic, measure dwell time, and analyze movement patters - all without needing invasive or manual methods.
With real-time tracking you can gain insights into peak hours, popular areas, and even queue lengths, which enables data-driven decisions to optimize store layout, inventory placement, and staffing.
Instance segmentation is an advanced computer vision task that combines object detection with pixel-level segmentation. It identifies and outlines individual objects within an image or video, providing detailed masks that delineate the boundaries of each object. This is particularly useful in applications where precise object boundaries are important, such as in autonomous driving, robotics, and medical imaging. By providing a richer representation of the visual world, instance segmentation enables tasks like object counting, tracking, and interaction analysis.
It is useful in cases where you need to measure the size of detected objects, like in drone image analysis. However, instance segmentation models are typically larger, slower, and less accurate than object detection models. Moreover, they require more data to train effectively, and the annotation process is significantly more laborious, with a harder training process. So, it is often better to try object detection first because it is easier and cheaper to test, and faster and more accurate to run.
Instance segmentation only must be used when the outline of the object is required by the application. Otherwise, object detection is enough for most cases.
Custom training is the process of fine-tunning a pre-trained model on a specific dataset so it can recognize objects that are not included in the original training dataset.
To train an instance segmentation model with DeepLab, you need to follow these steps: