Streamlining Amenity
Detection with AI

Challenge

The challenge was to streamline amenity detection at scale in a real estate setting using AI, with the goal of reducing the time and resources required for manual inspection and improving accuracy.

Solution

For the POC aimed at streamlining amenity detection with AI, our team at Labvent utilized the popular computer vision framework called Detectron2. This open-source platform is built on top of PyTorch and provides a simple interface for building, training, and deploying object detection models.

We implemented a custom object detection pipeline using Detectron2 that involved data preprocessing, model training, and model evaluation. The pipeline was specifically designed to detect various amenities within a given environment, such as swimming pools, tennis courts, and playgrounds.

To achieve this, we first collected and labeled a large dataset of images using a combination of manual and semi-automated techniques. We then trained the object detection model using the labeled data, and fine-tuned the model using transfer learning to improve its performance on our specific use case.

Finally, we evaluated the model on a separate test dataset to ensure that it was accurately detecting the amenities of interest. Overall, our POC showed promising results in streamlining amenity detection with AI and has the potential to be scaled to real-world applications.