Deep Learning
Deep learning based models have achieved the state of the art performance for image recognition and object detection tasks in the recent past. Many of these models are able to
This is a a gentle introduction to federated learning — a technique that makes machine learning more secure by training on decentralized data. We will also cover a real-life example
Imagine, one day you have an amazing idea for your machine learning project. You write down all the details on a piece of paper- the model architecture, the optimizer, the
In Machine Learning, we always want to get insights into data: like getting familiar with the training samples or better understanding the label distribution. To do that, we visualize the
Back in 2012, a neural network won the ImageNet Large Scale Visual Recognition challenge for the first time. With that Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton revolutionized the area
In the previous post, we learned how to classify arbitrarily sized images and visualized the response map of the network. In Figure 1, notice that the head of the camel
In this post, we will learn how to perform image classification on arbitrary sized images without using the computationally expensive sliding window approach. This post is written for people who
Imagine you trained a deep learning model on some dataset. A few days later, you want to reproduce the same experiment, but if you were not careful, you may never
In our previous post, we learned what is semantic segmentation and how to use DeepLab v3 in PyTorch to get an RGB mask of the detected labels within an image.
In this post, we will discuss the paper “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks” At the heart of many computer vision tasks like image classification, object detection, segmentation,
In this post, we will discuss the theory behind Mask RCNN Pytorch and how to use the pre-trained Mask R-CNN model in PyTorch. This post is part of our series
1. Image Classification vs. Object Detection Image Classification is a problem where we assign a class label to an input image. For example, given an input image of a cat,
