Uncertainty Estimation in Image Segmentation using Monte Carlo Dropout in PyTorch

In high-stakes fields like medical imaging, autonomous driving, and remote sensing, a wrong prediction made with high confidence can be catastrophic. That’s where Uncertainty Estimation steps in—empowering your model to express doubt. And with techniques like Monte Carlo Dropout, you can transform any deterministic deep network into a model that Continue Reading

Test Time Augmentation (TTA) for Segmentation in PyTorch

In recent posts, we’ve built a strong foundation around multiclass image segmentation using PyTorch. From creating segmentation masks, converting RGB to class index masks, overlaying results using OpenCV, to training a full-fledged UNet model and visualizing it with GradCAM, we’ve covered the full training pipeline. But what happens when your Continue Reading

GradCAM Heatmaps for Segmentation with UNet in PyTorch

In semantic segmentation, understanding how a deep learning model arrives at its decisions is crucial—especially in fields like medical imaging, agriculture, and autonomous systems. While U-Net and other architectures can deliver high accuracy, they often act as black boxes. In this blog post, we go beyond prediction accuracy. We’ll visualize Continue Reading

Multiclass Segmentation in PyTorch using U-Net

Semantic segmentation is a crucial task in computer vision that involves labeling each pixel in an image with its corresponding class. In this blog post, we’ll dive into building a multiclass semantic segmentation pipeline using the U-Net architecture with PyTorch. Our goal is to segment different types of weeds from Continue Reading

Overlay Mask on Image using OpenCV in Python

Overlaying a mask on top of an image is a common step in visualizing results from computer vision models, especially in tasks like semantic segmentation, object detection, and medical image analysis. This helps developers and researchers easily see which parts of the image the model has identified as belonging to Continue Reading

A Deep Dive into the Strengths and Limits of Large Language Models (LLMs)

Large language models (LLMs) have come to dominate natural‑language AI, and a new generation—Large Reasoning Models (LRMs)—now claims to “think” via extended chain‑of‑thought (CoT) outputs. But is this genuine reasoning or merely a high‑tech parlor trick? In their paper “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Continue Reading

Converting RGB Mask to Class Index Masks in Python

In the world of semantic segmentation, each pixel in an image carries a meaning — a class label that represents an object or region. These labels can be stored in various formats, and one common way is using a multi-class RGB mask, where each class is represented by a unique Continue Reading

Secure File Transfer with TCP Socket in Python

In today’s data-driven world, transferring files securely between machines is a common but critical task. Whether you’re syncing backup files, sharing sensitive documents, or building a peer-to-peer system, data security and integrity are paramount. In this tutorial, we’ll walk you through how to build a Secure File Transfer Application in Continue Reading

Top 10 Socket Programming Pitfalls in C and How to Avoid Them

Socket programming in C allows for low-level network communication and is foundational to many applications like web servers, file transfers, and messaging systems. However, mastering socket programming can be challenging due to C’s lack of abstraction, manual memory management, and error management. In this article, we’ll explore the top 10 Continue Reading