A computer vision task that identifies and classifies objects within images or video streams.

Stands for 'You Only Look Once', a real-time object detection system that processes images quickly.

A performance metric that measures the accuracy of positive predictions in object detection.

A performance metric that assesses the ability of a model to identify all relevant instances in a dataset.

A table used to evaluate the performance of a classification model, showing true vs. predicted classifications.

An open-source deep learning framework widely used for building and training neural networks.

An open-source machine learning library that provides a flexible platform for deep learning applications.

The capability of processing data instantly as it is received, crucial for applications like video surveillance.

A subset of machine learning that uses neural networks with multiple layers to analyze data.

A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process data.

The technique of cleaning and transforming raw data into a usable format for analysis.

A collection of data used to train a machine learning model, helping it learn patterns and make predictions.

A subset of data used during model training to tune hyperparameters and prevent overfitting.

Parameters set before the learning process begins, influencing the model's performance and training.

A modeling error that occurs when a model learns noise in the training data instead of the actual pattern.

A scenario where a model is too simple to capture the underlying trend of the data.

Quantitative measures used to assess the performance of a machine learning model.

Techniques to increase the diversity of training data by applying transformations like rotation and scaling.

A method where a pre-trained model is adapted to a new but related problem, saving time and resources.

The specific design and structure of the YOLO model, which includes layers for feature extraction and detection.

Use cases for object detection technology, including security, autonomous vehicles, and retail analytics.

The process of making small adjustments to a pre-trained model to improve its performance on a specific task.

A field of artificial intelligence that enables machines to interpret and make decisions based on visual data.

Self-driving cars that utilize computer vision and other technologies to navigate without human input.

Considerations regarding the moral consequences of deploying object detection technologies in society.