▮ GNNS For this post, I’d like to share the basics of Graph Neural Networks. ▮ Graphs First let’s look through the main elements of a graph in order to understand GNN’s. Nodes (Vertices) Edges: the connection between nodes Adjacency…
▮ Stable Diffusion There are several fine-tuning methods for text-to-image stable diffusion models and it is hard to intuitively understand the difference between them. So for this post, I’d like to share a visual representation of each fine-tuning method. ▮…
▮ Generative Model Trends I’ve had so many requests from multiple clients that they want to integrate generative models such as ChatGPT and Stable Diffusion into their current workflow. Despite the trend, one of the difficulties of adopting such models…
▮ My Mistakes I’ve finished a couple of POCs(Proof Of Concept; a phase where we create a prototype to check the feasibility of the machine learning project) this year as a Data Scientist/Project Manager for computer vision tasks, and I’d…
▮ 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…
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…
What It Does One-Shot learning can be useful when you want to identify someone just by giving the predicting model a single picture of them. Similarity Function A model can perform One-shot learning by learning a “similarity” function. When the…