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
GradCAM
GradCAM and its Implementation in PyTorch
Deep learning models, especially convolutional neural networks (CNNs), often function as black boxes, making it difficult to interpret their decision-making processes. Gradient-weighted Class Activation Mapping (GradCAM) is a powerful technique used to visualize and understand these models by highlighting the regions of an image that contribute most to a prediction. Continue Reading
GradCAM with TensorFlow: Interpreting Neural Networks with Class Activation Maps
Deep learning models, particularly convolutional neural networks (CNNs), are widely used for image classification, object detection, and various computer vision tasks. However, these models are often referred to as “black boxes” due to their complex decision-making processes. To interpret these decisions and understand what parts of an image influence the Continue Reading