Instance Segmentation Web Application Using Mask R-CNN and Flask

Instance segmentation is a computer vision task that involves detecting objects within an image and then classifying and segmenting each object individually. Mask R-CNN is a deep learning model that is commonly used for instance segmentation tasks. In this article, we will explore how to build a web application for instance segmentation using Mask R-CNN and Flask.

What is Mask R-CNN?

Mask R-CNN is an extension of the Faster R-CNN object detection model that also includes a mask prediction branch. This allows the model to not only detect objects within an image but also segment each object at the pixel level. Mask R-CNN has been widely adopted in tasks such as object detection, instance segmentation, and image segmentation.

Building the Web Application

To build our web application for instance segmentation using Mask R-CNN and Flask, we will first need to train the model on a dataset of annotated images. Once the model is trained, we can then deploy it to a Flask server to serve predictions over the web.

Flask is a lightweight web framework for Python that is commonly used for building web applications. We can create a Flask app that loads the pre-trained Mask R-CNN model and accepts image uploads from users. The app can then run the model on the uploaded image and return the segmented objects to the user.

Conclusion

Instance segmentation is a powerful computer vision task that can be used for a wide range of applications, such as object detection, image editing, and augmented reality. By using Mask R-CNN and Flask, we can easily build a web application that allows users to perform instance segmentation on their own images.

Overall, building a web application for instance segmentation using Mask R-CNN and Flask is a straightforward process that can greatly enhance the functionality and usability of computer vision applications. With the right tools and techniques, anyone can create their own instance segmentation web application with ease.