A-Z of Machine Learning and Computer Vision Terms

A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
  • This is some text inside of a div block.
  • This is some text inside of a div block.
  • This is some text inside of a div block.
  • This is some text inside of a div block.
  • This is some text inside of a div block.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
PyTorch
PyTorch
Q
Q
Quantum Machine Learning
Quantum Machine Learning
Query Strategy (Active Learning)
Query Strategy (Active Learning)
Query Synthesis Methods
Query Synthesis Methods
R
R
RAG Architecture
RAG Architecture
ROC (Receiver Operating Characteristic) Curve
ROC (Receiver Operating Characteristic) Curve
Random Forest
Random Forest
Recall (Sensitivity or True Positive Rate)
Recall (Sensitivity or True Positive Rate)
Recurrent Neural Network (RNN)
Recurrent Neural Network (RNN)
Region-Based CNN (R-CNN)
Region-Based CNN (R-CNN)
Regression (Regression Analysis)
Regression (Regression Analysis)
Regularization Algorithms
Regularization Algorithms
Reinforcement Learning
Reinforcement Learning
Responsible AI
Responsible AI
S
S
Scale Imbalance
Scale Imbalance
Scikit-Learn
Scikit-Learn
Segment Anything Model (SAM)
Segment Anything Model (SAM)
Selective Sampling
Selective Sampling
Self-Supervised Learning
Self-Supervised Learning
Semantic Segmentation
Semantic Segmentation
Semi-supervised Learning
Semi-supervised Learning
Sensitivity and Specificity of Machine Learning
Sensitivity and Specificity of Machine Learning
Sentiment Analysis
Sentiment Analysis
Sliding Window Attention
Sliding Window Attention
Stream-Based Selective Sampling
Stream-Based Selective Sampling
Supervised Learning
Supervised Learning
Support Vector Machine (SVM)
Support Vector Machine (SVM)
Surrogate Model
Surrogate Model
Synthetic Data
Synthetic Data
T
T
Tabular Data
Tabular Data
Text Generation Inference
Text Generation Inference
Training Data
Training Data
Transfer Learning
Transfer Learning
Transformers (Transformer Networks)
Transformers (Transformer Networks)
Triplet Loss
Triplet Loss
True Positive Rate (TPR)
True Positive Rate (TPR)
Type I Error (False Positive)
Type I Error (False Positive)
Type II Error (False Negative)
Type II Error (False Negative)
U
U
Unsupervised Learning
Unsupervised Learning
V
V
Variance (Model Variance)
Variance (Model Variance)
Variational Autoencoders
Variational Autoencoders
W
W
Weak Supervision
Weak Supervision
Weight Decay (L2 Regularization)
Weight Decay (L2 Regularization)
X
X
XAI (Explainable AI)
XAI (Explainable AI)
XGBoost
XGBoost
Y
Y
YOLO (You Only Look Once)
YOLO (You Only Look Once)
Yolo Object Detection
Yolo Object Detection
Z
Z
Zero-Shot Learning
Zero-Shot Learning
F

Frames Per Second (FPS)

FPS (Frames Per Second) is a unit that measures how many distinct consecutive frames (images) are displayed or processed in one second in a video or real-time system. This metric is crucial both in video content (for viewer experience of smoothness) and in performance benchmarking of vision systems (processing FPS indicates how fast an algorithm can run). For example, a tracking algorithm might operate at 10 FPS on a given hardware setup, meaning it can analyze 10 video frames each second. In the context of video games or AR/VR, FPS is a key performance indicator – e.g., aiming for 60 FPS or more to ensure smooth and responsive visuals. In video files, FPS is stored as part of the format (like 29.97 for NTSC). Note that “FPS” in vision can mean either the frame rate of video content or the throughput of an algorithm processing frames.

Further Reading

🔗  Research Paper📄  Blog Post📄  Blog Post

Explore Our Products

Lightly One

Data Selection & Data Viewer

Get data insights and find the perfect selection strategy

Learn More

Lightly Train

Self-Supervised Pretraining

Leverage self-supervised learning to pretrain models

Learn More

Lightly Edge

Smart Data Capturing on Device

Find only the most valuable data directly on devide

Learn More

Ready to Get Started?

Experience the power of automated data curation with Lightly

Learn More
Class Boundary (Statistics & Machine Learning)
Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN)
Large Language Model (LLM)
Chain of Thought (CoT)
Foundation Models
Semantic Segmentation
Variance (Model Variance)
XAI (Explainable AI)
YOLO (You Only Look Once)
Weight Decay (L2 Regularization)
Text Generation Inference
True Positive Rate (TPR)
Type II Error (False Negative)
Type I Error (False Positive)
Triplet Loss
Transformers (Transformer Networks)
Tabular Data
Stream-Based Selective Sampling
Synthetic Data
Training Data
Support Vector Machine (SVM)
Sentiment Analysis
Surrogate Model
Supervised Learning
Semi-supervised Learning
Selective Sampling
Sliding Window Attention
Sensitivity and Specificity of Machine Learning
Scikit-Learn
Segment Anything Model (SAM)
Regularization Algorithms
ROC (Receiver Operating Characteristic) Curve
Scale Imbalance
Responsible AI
Regression (Regression Analysis)
Region-Based CNN (R-CNN)
Recall (Sensitivity or True Positive Rate)
Random Forest
RAG Architecture
Query Synthesis Methods
Query Strategy (Active Learning)
Predictive Model Validation
Prompt Injection
Prompt Engineering
PyTorch
Prompt Chaining
PR AUC
Precision
Pooling
Pose Estimation
Pool-Based Sampling
Pattern Recognition
Perceptron
Pixel
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Pandas and NumPy
Panoptic Segmentation
PACS (Picture Archiving and Communication System)
Overfitting
Outlier Detection
Object Tracking
Optical Character Recognition (OCR)
One-Shot Learning
Object Localization
Object Detection
Normalization
Noise
NIfTI
Natural Language Processing (NLP)
Neural Networks
Multi-Task Learning
Motion Detection
Motion Estimation
Model Validation
Latent Dirichlet Allocation (LDA)
Medical Image Segmentation
Model Parameters
Model Accuracy
Micro-Models
Mean Squared Error (MSE)
Keypoints
Mean Average Precision (mAP)
Machine Learning (ML)
Loss Function
Linear Regression
Linear Discriminant Analysis (LDA)
LIDAR
Learning Rate
Label Errors
Label
Intersection over Union (IoU)
Interpolation
Interpretability
Imbalanced Dataset
Instance Segmentation
Image Processing
Image Restoration
Image Segmentation
Image Recognition

Get the most out of your data

Lightly optimizes your model training by selecting the most valuable data, reducing redundancy and bias while improving performance.

Get in contact with us
Company & Product
Privacy terms
Postal Address
Social
info@lightly.ai
Copyright © 2024 Lightly AG. All rights reserved.