This is the second blog in the series Deploying a Multi-Label Image Classifier using PyTorch, Flask, ReactJS and Firebase data storage. If you missed the first blog its here. Before, starting with this blog be sure to go through the previous one.
In this blog we will be developing a flask app/service to create an API for our ReactJS front-which we will be building in next part.
The whole code base is shared here.
Go to your Unix/Linux based terminal and use the following line to install the required modules. …
This is the first blog from the series of blogs based on building deep learning models and taking them to production.
The code included in the blog post can be found here.
Let’s define Multi-Label classification, we can consider this problem of multi-label classification as Multiple Binary Class Classification. In layman’s terms, supposedly, there are 20 different class labels in a dataset of images. …
I know there are many blogs about CNN and multi-class classification, but maybe this blog wouldn’t be that similar to the other blogs. Yes, it does have some theory, and no the multi-class classification is not performed on the MNIST dataset. In this blog, multi-class classification is performed on an apparel dataset consisting of 15 different categories of clothes. The classes will be mentioned as we go through the coding part. The contents and links to various parts of the blogs are given below,