PyTorch
The YOLO (You Only Look Once) series of models, renowned for its real-time object detection capabilities, owes much of its effectiveness to its specialized loss functions. In this article, we
With millions of trainable parameters, neural networks have long been considered black boxes. They can produce stunning results, and we often accept the output with very little understanding as to
Unveiling a significant breakthrough in computer vision, Deci introduces YOLO-NAS Pose, the latest evolution in Pose Estimation technology. Building on the foundations of the acclaimed YOLO-NAS, this advanced model stands
This article shows the steps for deploying a deep learning model on HuggingFace Spaces using Gradio.
In this article, we train YOLOv8 on a custom pothole detection dataset using the Ultralytics YOLO package.
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
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.
Moving away from traditional document scanners, learn how to create a Deep Learning-based Document Segmentation model using DeepLabv3 architecture in PyTorch.
You can scarcely find a good article on deploying computer vision systems in industrial scenarios. So, we decided to write a blog post series on the topic. The topics we
you will learn how to train your own fast style transfer network in pytorch and deploy the model to get live style transfer effect on a web meeting on zoom/Skype/
