▮ Low GPU Utilization Ideally, we want to use our GPU machines during training/inference fully. However, if you are not considering GPU utilization when creating training scripts for your deep learning model, the odds are that the GPU utilization rate…
▮ Stack Structure For this post, I’d like to share an intuitive overview of the basic steps of how your code communicates with the GPU when implementing deep learning models. Starting from the GPU side, the general process takes the…
▮ Elements Metric learning aims to measure the similarity between samples while using distance metrics for learning. Due to a survey in 2019, this field seems to become more and more important. So for this post, I’d like to share…
▮ Image Classification When doing image classification tasks, there are mainly 2 approaches; Traditional classification and Metric Learning. Here are the differences between the two approaches. ▮ Traditional Classification The traditional classification approach classifies images by outputting the probability for…
▮ Execution Methods Execution methods can become confusing to people who have just started machine learning. So for this post, I’d like to share the difference between graph execution and eager execution. ▮ Graph Execution Fig.1 – Graph Execution Graph…
Difference Transfer learning and Fine-tuning both replace the final layer to make predictions for a different task. However, while transfer learning fixes all the layer previous to the final output layer, fine-tuning initializes the weights with the pre-trained weights but…
Preparing Your Dataset For Training When training a multi-class segmentation model, loss functions such as the CrossEntropyLoss() in Pytorch require the mask to be categorically labeled. As in the image above, the RGB mask (middle) is properly segmented but all…
When applying data augmentation for computer vision models, it is important to apply the same augmentation in the same order for both the input and target image. Here is one way to implement using Albumentation and Pytorch. Implementation Today I’ll…
Clustering Depending on your data and objective, you may not even have to train a deep-learning model for image segmentation. Here is one way to apply segmentation using Kmeans clustering. Implementation from sklearn.cluster import KMeans from matplotlib.image import imread import…
Anomaly Detection Anomaly detection in videos refers to the identification of events that do not align with the expected behavior. This paper is a pioneering work that leverages the difference between a predicted future frame and its ground truth to…