🎉 Big news: LightlyTrain now supports DINOv2. Read our announcement.

A-Z of Machine Learning and Computer Vision Terms

Artificial Intelligence (AI)

Binary Classification

Canonical Correlation Analysis (CCA)

Case-Based Reasoning

Chain of Thought (CoT)

ChatGPT

Chi-Squared Automatic Interaction Detection (CHAID)

Class Boundary (Statistics & Machine Learning)

Class Imbalance

Clustering

Collaborative Filtering

Computer Vision

Computer Vision Model

Concept Drift

Conditional Random Field (CRF)

Confusion Matrix

Constrained Clustering

Contrastive Learning

Convolutional Neural Network (CNN)

Deep Reinforcement Learning (DRL)

Dimensionality Reduction

Dropout

Dynamic and Event-Based Classifications

Expectation-Maximization Algorithm (EM)

Extreme Learning Machine

Fisher’s Linear Discriminant

Foundation Models

Frame Rate

Frames Per Second (FPS)

Fully Connected Layer

Fuzzy Logic

Generative Adversarial Network (GAN)

Bounding Box

In computer vision, a bounding box is a rectangular coordinate system used to localize and delineate an object within an image or video frame. It is typically defined by the coordinates of its top-left corner (x1, y1) and its bottom-right corner (x2, y2), or by its center coordinates, width, and height. Bounding boxes are a fundamental component in object detection tasks, where the goal of a model is not only to classify what objects are present in an image but also to accurately identify their precise locations. The prediction of accurate bounding boxes is a key metric for evaluating the performance of object detection models, forming the basis for metrics like Intersection Over Union (IOU), which measures the overlap between predicted and ground-truth bounding boxes.