Deep Learning
In this article, we show how to implement Vision Transformer using the PyTorch deep learning library.
In this article, we use ImageNet pre-trained CNN models for image classification tasks.
In this article, we train YOLOv8 on a custom pothole detection dataset using the Ultralytics YOLO package.
In this article, we explore the Diffusion models for Image generation and art generation. We cover models like Dall-E 2, Imagen, Stable Diffusion, and Midjourney
This article discusses the working of Convolutional Neural Networks on depth for image classification along with diving deeper into the detailed operations of CNN.
In this article, we cover the basics of training neural networks for beginners for an image classification problem.
In this article, we explore the YOLOv5 instance segmentation architecture and run inference on several videos and images.
DeepLab models, first debuted in ICLR ‘14, are a series of deep learning architectures designed to tackle the problem of semantic segmentation. After making iterative refinements through the years, the
Build an AI fitness trainer application that analyzes squats using MediaPipe’s Pose solution and prompts appropriate feedback.
YOLOR, inspired by how humans combine knowledge, is an object detection model that pushes the boundaries of real-time detection with improved speed & accuracy.
This article explains several performance comparison between different YOLO object detection models. These include YOLOv5, YOLOv6, and YOLOv7.
In this article we train the YOLOv6 Nano, Small, and Large models on a custom Underwater Trash Detection dataset and compare the results with YOLOv5 and YOLOv7.
