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
PyTorch
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
What is Intersection over Union (IoU) in Object Detection?
Intersection over Union (IoU) is a popular evaluation metric used in the field of computer vision and object detection. It is used to calculate the overlap between two bounding boxes and is used to evaluate the accuracy of object detection algorithms. IoU is a value between 0 and 1 that Continue Reading
TensorFlow vs PyTorch
TensorFlow and PyTorch are both popular open-source frameworks for building and training machine learning models. Both frameworks have their own strengths and weaknesses, and the choice between them depends on the specific needs of the project. Introduction to TensorFlow and PyTorch TensorFlow TensorFlow, which was developed by Google, is a Continue Reading
Squeeze and Excitation Implementation in TensorFlow and PyTorch
The Squeeze and Excitation network is a channel-wise attention mechanism that is used to improve the overall performance of the network. In today’s article, we are going to implement the Squeeze and Excitation module in TensorFlow and PyTorch. What is Squeeze and Excitation Network? The squeeze and excitation attention mechanism Continue Reading