Faster R-CNN Explained for Object Detection Tasks
Learn how Faster R-CNN works for object detection tasks with its region proposal network and end-to-end architecture.
Implementation StyleGAN1 from scratch
In this article, we will make a clean, simple, and readable implementation of StyleGAN using PyTorch.
Stable Diffusion Tutorial Part 2: Using Textual Inversion Embeddings to gain substantial control over your generated images
Learn how to train textual inversion for Stable Diffusion in a Jupyter Notebook and generate samples that represent the features of the training images.
A Guide to the DataLoader Class and Abstractions in PyTorch
We will explore one of the biggest problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.
Visual Question Answering: a Survey
URL: https://www.progressiverobot.com/introduction-to-visual-question-answering/ We've seen a lot of advancements in the last few years in many subdomains of machine learning. Computer vision tasks like object detection and image segmentation, as well as NLP tasks like entity recognition, language generation, and question answering, are now being solved by neural networks and approached much differently, with more speed […]
Evaluating Object Detection Models Using Mean Average Precision (mAP)
URL: https://www.progressiverobot.com/mean-average-precision/ To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections. In my last article we looked in detail at […]
A Complete Guide to Image Super-Resolution in Deep Learning and AI
Understand the latest techniques, models, and applications of image super-resolution in deep learning and computer vision. A comprehensive guide for researchers and developers.
Channel Attention and Squeeze-and-Excitation Networks (SENet)
In this article we will cover one of the most influential attention mechanisms proposed in computer vision: channel attention, as seen in Squeeze-and-Excitation Networks (SENet).
Stable Diffusion 3.5 Large with GPU cloud servers
In this article, we show how to use Stable Diffusion 3.5 Large image generation models with GPU cloud servers.
Capsule Networks: A Quick Primer
Introduction Whether you're new to deep learning or a serious researcher, you've surely encountered the term convolutional neural networks (CNNs). They are one of the most researched and top-performing architectures in the field. That being said, CNNs have a few drawbacks in recognizing features of input data when they are in different orientations. To address […]