Welcome to this project on Building a CNN Classifier using TensorFlow 2 for MNIST Fashion Dataset. In this project, we will understand how to use the TensorFlow 2 platform to build a simple classifier using Convolutional Neural Networks(CNNs).
CNNs have been one of the state-of-art tools in the current era of Computer Vision. The present-day deep learning and computer vision communities find numerous applications of CNNs in classification tasks and object detection use cases. These use cases have formed the basis of many real-time applications like self-driving cars, face recognition apps, satellite photo analysis and classification, and many more …
Welcome to the project on Mask R-CNN with OpenCV for Object Detection. In this project, we will learn how to read a pre-trained TensorFlow model for object detection using OpenCV.
The real-world scenarios have a lot of applications based on object detection. For example, object detection models are used in self-driving cars to recognize where the pedestrians are, where the are vehicles located, where the signals are, etc in the given frame of view. So, it is very important to develop an understanding of how to use a pre-trained object detection model so that we could later customize it based …
Welcome to the project on Hosting an Image Classification App on Heroku. In this project, we will get a basic understanding of how to deploy a web app on Heroku, a Platform as a Service.
Heroku is a cloud platform for the deployment and management purposes of web applications. It could be considered as one of the best solutions for hosting web-apps very quickly, thus allowing the developer to concentrate more on development.
Welcome to the project on How to build low-latency deep-learning-based flask app. In this project, we will refactor the entire codebase of the project [ How to Deploy an Image Classification Model using Flask]. That monolithic code will be refactored to form two microservices - the flask service and model service. The model service acts as a server that renders pretrained Tensorflow model as a deep learning API, and keeps listening for any incoming requests. The flask service requests the model service, and displays the response from the model server. This way, we write cleaner code and promote service isolation.