A-Z of Machine Learning and Computer Vision Terms
A
AI Agent
AI Assistants
AI Assisted Labeling
Active Learning
Algorithm
Anchor Boxes
Anomaly Detection
Artificial Intelligence (AI)
Attribute
B
Backpropagation
Bagging
Batch
Batch Normalization
Bayesian Network
Bias
Big Data
Binary Classification
Blur
Boosting
Bounding Box
C
COCO
Calibration
Calibration Curve
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Chain of Thought (CoT)
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary
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)
Convolutional Neural Networks (CNNs)
Cross-Validation
D
DICOM
Data Approximation
Data Augmentation
Data Drift
Data Error
Data Mining
Data Operations
Data Pre-processing
Data Quality
Dataset
Decision Boundary
Decision List
Decision Stump
Decision Tree
Deep Learning
Deep Neural Networks
Dimensionality Reduction
Dropout
Dynamic and Event-Based Classifications
E
Edge Cases
Edge Computing
Edge Detection
Elastic Net
Embedding Spaces
Ensemble Learning
Epoch
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
F
F1 Score
FP-Growth Algorithm
Factor Analysis
False Positive Rate
Feature
Feature Engineering
Feature Extraction
Feature Hashing
Feature Learning
Feature Scaling
Feature Selection
Feature Vector
Few-shot Learning
Fisher’s Linear Discriminant
Foundation Models
Frame Rate
Frames Per Second (FPS)
Fully Connected Layer
Fuzzy Logic
G
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Pre-Trained Transformer
Yolo Object Detection
YOLO is a family of popular single-stage object detection models known for their speed and accuracy in real-time detection tasks. “You Only Look Once” means the model predicts bounding boxes and class probabilities for objects in an image in a single forward pass of the network, rather than region-by-region or in multiple stages
YOLO reframes object detection as a direct regression problem: it divides the image into a grid and, in one evaluation, outputs predictions for object bounding boxes and their classes for each region. This design makes YOLO extremely fast compared to two-stage detectors like R-CNN. Over successive versions (YOLOv1 through YOLOv8 and beyond), the architecture and training techniques have improved, yielding state-of-the-art results in object detection
YOLO models are widely used in applications requiring real-time processing, such as autonomous driving or live video analytics, where the benefit of detecting objects “all at once” is crucial.
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