LearnOpenCV

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Meet YOLO-NAS: New YOLO Object Detection Model Beats YOLOv6 & YOLOv8

YOLO-NAS is the new real-time SOTA object detection model. YOLO-NAS models outperform YOLOv7, YOLOv8 & YOLOv6 3.0 models in terms of mAP an

[learnopencv1]

An In-Depth Guide to Denoising Diffusion Probabilistic Models – From Theory to Implementation

An in-depth explanation of the theory and math behind denoising diffusion probabilistic models (DDPMs) and implementing them from scratch in

[learnopencv1]

Understanding Attention Mechanism in Transformer Neural Networks

In this article, we cover the attention mechanism in neural networks in detail and also implement it using PyTorch

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Deploying a Deep Learning Model using Hugging Face Spaces and Gradio

This article shows the steps for deploying a deep learning model on HuggingFace Spaces using Gradio.

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Train YOLOv8 on Custom Dataset – A Complete Tutorial

In this article, we train YOLOv8 on a custom pothole detection dataset using the Ultralytics YOLO package.

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Introduction to Diffusion Models for Image Generation – A Comprehensive Guide

In this article, we explore the Diffusion models for Image generation and art generation. We cover models like Dall-E 2, Imagen, Stable Diff

[learnopencv1]

FCOS- Anchor Free Object Detection Explained

FCOS: Fully Convolutional One-stage Object Detection is an anchor-free (anchorless) object detector. Inference on image and video with PyTo

[learnopencv1]

YOLOv6 Custom Dataset Training – Underwater Trash Detection

In this article we train the YOLOv6 Nano, Small, and Large models on a custom Underwater Trash Detection dataset and compare the results wit

[learnopencv1]

What is EXIF Data in Images?

Find out what is EXIF and how EXIF data can be used in various applications. Using EXIF to visualize potholes of a city on google maps.

[learnopencv1]

What is EXIF Data in Images?

Find out what is EXIF and how EXIF data can be used in various applications. Using EXIF to visualize potholes of a city on google maps.

EXIF data (Exchangeable Image File Format) contains information on image and audio files. It is required by image viewers or audio players to sort the files, display thumbnails, load camera information, and add other functionalities. However, EXIF is not limited to basic image attributes. Using EXIF tags, you can find the name of the person who captured the image, the location, whether the image has been edited, and much more.

In this article, we will break down EXIF metadata and show how to access and modify it. We will also walk through an application that visualizes potholes of a city in Google Maps using EXIF tags.

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YOLOv7 Object Detection Paper Explanation and Inference

A technical review of YOLOv7 paper along with inference report. YOLOv7 Pose detection code included.

YOLOv7 is a single-stage real-time object detector. It was introduced to the YOLO family in July’22. According to the YOLOv7 paper, it is the fastest and most accurate real-time object detector to date. YOLOv7 established a significant benchmark by taking its performance up a notch.

[learnopencv1]

Fine Tuning YOLOv7 on Custom Dataset

This article explains the training pipeline for fine tuning of the YOLOv7 object detection model on a custom pothole detection dataset

Since its inception, the YOLO family of object detection models has come a long way. YOLOv7 is the most recent addition to this famous anchor-based single-shot family of object detectors. It comes with a bunch of improvements which include state-of-the-art accuracy and speed.  In this article, we will be fine tuning the YOLOv7 object detection model on a real-world pothole detection dataset.

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What is Face Detection? – The Ultimate Guide for 2022

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

Face Detection is a computer vision technique in which a computer program can detect the presence of human faces and also find their location in an image or a video stream. Isn’t it mind-boggling how the mobile camera automatically detects your face every time you try to take a selfie? You must’ve also noted that it captures other people’s faces in the frame. Well, all this wouldn’t have been possible without Face Detection algorithms. With every passing year, Facial Detection algorithms are evolving to be faster and more robust.

In this post, you will get an overview of Face Detection itself. We will walk through various state-of-the-art Face Detectors and how they evolved over time.

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Object Tracking and Reidentification with FairMOT

Explaining and understanding the inner workings of FairMOT Tracker. Checkout the intermediate outputs, and compare the results with DeepSort

Arguably, the most crucial task of Deep Learning-based Multiple Object Tracking (MOT) is not to identify an object but to re-identify it after occlusion. There are a plethora of trackers available to use, but not all of them have a good re-identification pipeline. In this blog post, we will focus on one such tracker, FairMOT, that revolutionized the joint optimization of detection and re-identification tasks in tracking.

[learnopencv1]

Driver Drowsiness Detection Using Mediapipe In Python

Driver drowsiness detection systems help reduce mishaps due to tired or sleepy drivers. Learn to build such a robust system using MediaPipe

Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. In this article, we will create a drowsy driver detection system to address such an issue. For this, we will use Mediapipe’s Face Mesh solution in python and the Eye Aspect ratio formula. Our goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time.

In this post, we will:

Learn how to detect eye landmarks using the Mediapipe Face Mesh solution pipeline in python.

Introduce and demonstrate the Eye Aspect Ratio (EAR) technique. 

Create a Driver Drowsiness Detection web application using streamlit.

Use streamlit-webrtc to help transmit real-time video/audio streams over the network. 

Deploy it on a cloud service.

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Driver drowsiness detection system alerts the driver if they feel drowsy or fall asleep behind the wheel. Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. We will use Mediapipe’s Face Mesh solution in python and the Eye Aspect ratio formula. Our goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time. We will discuss the following: ✅Learn how to detect eye landmarks using the Mediapipe Face Mesh solution pipeline in python. ✅Introduce and demonstrate the Eye Aspect Ratio (EAR) technique. ✅Create a Driver Drowsiness Detection web application using streamlit. ✅Use streamlit-webrtc to help transmit real-time video/audio streams over the network. ✅Deploy it on a cloud service. ❓FAQ How does driver drowsiness detection work? Which algorithm is used for driver drowsiness detection? How can we detect when a driver is falling asleep?