Computer Vision

In this blog post we review the YOLOv6 paper, carry out inference using the YOLOv6 models, and also compare YOLOv6 with YOLOv5.
Driver drowsiness detection systems help reduce mishaps due to tired or sleepy drivers. Learn to build such a robust system using MediaPipe in Python.
Explaining and understanding the inner workings of FairMOT Tracker. Checkout the intermediate outputs, and compare the results with DeepSort Tracker
Let's understand what face detection is, how it works, what its challenges are, and in what areas face detection is used. You will also see the journey of face detection
Moving away from traditional document scanners, learn how to create a Deep Learning-based Document Segmentation model using DeepLabv3 architecture in PyTorch.

Surveillance cameras plays an essential role in securing our home or business. These cameras are super affordable. So is setting up a surveillance system. The only difficult and expensive part

A tutorial for using Mediapipe’s Face-Mesh to create Augmented Reality Facial Filters. We are sharing the code in Python.

A conventional video or picture captures the three-dimensional world in two dimensions, losing crucial information regarding depth, which many applications now demand. Depth estimation is a challenging problem, and there

The AI community generously shares code, model architectures, and even models trained on large datasets. We are standing on the shoulders of giants, which is why the industry is adopting

The Intel-OpenVINO Toolkit provides many great functionalities for Deep-Learning model optimization, inference and deployment. Perhaps the most interesting and practical tool among them is the Deep-Learning (DL) workbench. Not only

Traditionally, Deep-Learning models are trained on high-end GPUs. But for inference, Intel CPUs and edge devices like NVidia’s Jetson and Intel-Movidius VPUs are preferred. Most of these Intel CPUs come

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