In the previous post, we learned how to apply a fixed number of tags to images. Let’s now switch to this broader task and see how we can tackle it. In many real-life tasks, there is a ...
CNN Receptive Field Computation Using Backprop
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 is almost not ...
Ensuring Training Reproducibility in PyTorch
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 be able to reproduce the same ...
Applications of Foreground-Background separation with Semantic Segmentation
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. Recall that semantic segmentation is ...
EfficientNet: Theory + Code
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 ...
Mask R-CNN Instance Segmentation with PyTorch
In this post, we will discuss the theory behind Mask R-CNN and how to use the pre-trained Mask R-CNN model in PyTorch. This post is part of our series on PyTorch for Beginners. 1. Semantic ...