Implementation StyleGAN1 from scratch
In this article, we will make a clean, simple, and readable implementation of StyleGAN using PyTorch.
How did I train YOLOv12 on a Custom Dataset with GPUs
Train YOLOv12 on a custom dataset using GPU cloud servers. Follow this guide for setup, configuration, and scalable model training.
How Convolutional Autoencoders Power Deep Learning Applications
Explore autoencoders and convolutional autoencoders. Learn how to write autoencoders with PyTorch and see results in a Jupyter Notebook
Introducing the YOLOv8 Web UI
Introducing the new YOLOv8 Web UI – image labeling, training, and inference in a single GUI.
How to Train YOLO v5 on a Custom Dataset | the cloud provider
Learn how to train YOLOv5 on a custom dataset with this step-by-step guide. Discover data preparation, model training, hyperparameter tuning, and best practices for object detection.
Data Augmentation for Bounding Boxes: Rotation and Shearing
This article will cover how to implement rotation and shear images and bounding boxes using OpenCV’s affine transformation features.
Introduction to DETR – Part 1
DETR introduces a completely new architecture, setting a new standard in the object detection field. In this article, we explore the Detection Transformer (DETR) concept, highlighting its groundbreaking approach and the significant advancements it brings to object detection technology.
Step-by-step instructions for training YOLOv7 on a Custom Dataset
Follow these step-by-step instructions to learn how to train YOLOv7 on custom datasets, and then test it with our sample demo on detecting objects with the Road Sign Detection dataset with Gradient’s Free GPU Notebooks
Why Data Augmentation Is Key for Vision, Language, and Audio AI
Explore data augmentation techniques that improve accuracy, robustness, and generalization in vision, language, and audio models.
Introduction to DETR – Part 2: The Crucial Role of the Hungarian Algorithm
In part 2 of this tutorial series, we look at DETR’s Hungarian Algorithm in depth to show how it minimizes cost.
