Ultralytics YOLO models
Power vision AI solutions with Ultralytics YOLO models, enabling fast and accurate real-time analysis of images and videos through efficient computer vision tasks.
Trusted by the world's leading organizations
Try YOLO26 Inference
Drag and drop an image to see real-time object detection
Input Image
Upload an image or select an example to test yolo26n. Predict Docs
Drop an image here
or click to browse
Supports JPEG, PNG, WebP, AVIF, HEIC, JP2, TIFF, BMP & more (max 10MB)
Confidence
0.25
IoU
0.70
Image Size
640
API Docs
Deploy your model to get a dedicated inference endpoint with API key authentication — then replace the placeholder URL and key below with your deployment values. Deploy docs
Note: This is example code. Deploy your model first to get your endpoint URL and API key.
import requests
# Replace with your deployment URL and API key
url = "https://your-deployment-url.run.app/predict"
api_key = "YOUR_API_KEY"
# Optional inference parameters (conf, iou, imgsz)
args = {"conf": 0.25, "iou": 0.7, "imgsz": 640}
with open("image.jpg", "rb") as f:
response = requests.post(
url,
headers={"Authorization": f"Bearer {api_key}"},
data=args,
files={"file": f},
)
print(response.json())Our models' impact
Streamline processes across industries with our cutting-edge vision AI models. Speed, accuracy and ease-of-use powered by Ultralytics.
134.5K+
GitHub stars
304.7M+
Downloads
3.3B+
Ultralytics YOLO usages / day
1.1K+
Open-source contributors
The evolution of YOLO models
From the original YOLO architecture to Ultralytics YOLO26, the model family has continued to improve real-time computer vision workflows.
2015
YOLO
Developed by Joseph Redmon and team.
2016
YOLOv2
Created by Joseph Redmon and Ali Farhadi.
2018
Developed by Joseph Redmon and Ali Farhadi.
2018
Developed by Alexey Bochkovskiy and team.
YOLOv5 redefined object detection with fast, accurate performance and a user-friendly PyTorch implementation.
2022
Introduced by Meituan and team.
2022
Released by Chien-Yao Wang and team.
YOLOv8 expanded support to instance segmentation, classification, pose estimation, and other core vision tasks.
2024
Developed by Chien-Yao Wang and team.
2024
Designed by Ao Wang for Tsinghua University.
YOLO11 improved accuracy, precision, and speed while preserving the familiar Ultralytics workflow.
YOLO26 is lighter, smaller, and faster, with the nano variant delivering up to 43% faster CPU inference.
Ultralytics YOLO models at a glance
Compare recent Ultralytics YOLO model families across speed, accuracy, and supported vision tasks.
| Features | ||||
|---|---|---|---|---|
| Speed Image processing time | 1.06 ms | 0.99 ms | 1.5 ms | 1.7 ms |
| Accuracy mAP50-95 | 34.3% | 37.3% | 39.5% | 40.9% |
| Object detection | ||||
| Image classification | - | |||
| Instance segmentation | - | |||
| Semantic segmentation | - | - | ||
| Pose estimation | - | |||
| OBB detection | - |
Why choose Ultralytics YOLO?
Optimized for edge and cloud deployment
Bring fast, reliable results to real-time vision AI projects across edge devices, cloud infrastructure, and hybrid systems.
Seamless integration with built-in security
Integrate YOLO models across platforms and devices while keeping data local and workflows protected.
Clear licensing and compliance
Choose from academic, open-source, and enterprise licensing paths to match your deployment needs.
Backed by a global community
Dedicated support channels, active forums, and regular updates give teams the resources they need to succeed.
State-of-the-art accuracy and efficiency
Ultralytics keeps improving model performance through research focused on accuracy, efficiency, and streamlined deployment.
See our models in action
Glacier Robotics reduced PET leakage by 70% across US recycling facilities
See how Glacier Robotics uses Ultralytics YOLO11 to reduce PET leakage by 70%, improve recycling accuracy, and automate waste sorting.
SOHGA cuts parking monitoring time by 30% with Ultralytics YOLO
Learn how SOHGA's MEGURU system uses Ultralytics YOLO26 to automate parking lot patrols, reduce patrol time by 30%, and improve safety.
Scaleout cuts model updates from weeks to hours with Ultralytics YOLO
Discover how Scaleout uses Ultralytics YOLO and federated learning to fine-tune AI models on edge devices while keeping sensitive data secure.
RapiD Engineering deploys seafood quality control 1 week faster with Ultralytics YOLO
See how RapiD Engineering uses Ultralytics YOLO to automate salmon inspection, detect defects in real time, and save a week of engineering work.
Project Ocean Oasis advances reef conservation with Ultralytics YOLO
Discover how Project Ocean Oasis uses Ultralytics YOLO, edge AI, and autonomous monitoring systems to scale reef conservation and ocean intelligence.
Volley powers 250+ on-court AI trainers with Ultralytics YOLO
"What's really nice is that the model performs really well in real time on edge hardware on the trainer, and we can use the same model in the cloud to run the exact same flow."
WG Tech Solutions cuts safety violations by 28% with Ultralytics YOLO and Axelera’s AI Accelerator
Learn how WG Tech Solutions managed to reduce safety violations in manufacturing by 28% leveraging Ultralytics YOLO and Axelera’s AI Accelerator
Stride delivers 1-minute equine gait analysis with Ultralytics YOLO
Learn how Stride leverages Ultralytics YOLO models for horse pose estimation, achieving a full gait analysis in under 1 minute.
Pixelabs achieves 95% recall with Ultralytics YOLO-driven automation
Learn how Pixelabs leverages Ultralytics YOLO models to automate workflows and achieve 95% recall.
SiteAssist improves site safety by processing 770K+ images with Ultralytics YOLO
Learn how SiteAssist leverages Ultralytics YOLO models to improve construction site safety.
Chef Robotics uses Ultralytics YOLO to cut food giveaway by 67%
Explore how Chef Robotics uses Ultralytics YOLO models for precise food assembly.
Cali Intelligence shortens checkout queues by 43% with Ultralytics YOLO
Explore how Cali Intelligence uses Ultralytics YOLO models to reduce retail checkout queues with object detection.
MarineSitu hits 96%+ uptime in underwater monitoring using Ultralytics YOLO
Discover how MarineSitu uses Ultralytics YOLO to transform underwater object detection.
Theia Scientific speeds up microscopy analysis 43× with Ultralytics YOLO
Discover how Theia Scientific uses Ultralytics YOLO to redefine microscopy data analysis.
eSmart Systems halves power line inspection time with Ultralytics YOLO
Discover how eSmart Systems uses Ultralytics YOLO to boost defect detection speed and transform utility inspections.
Axelera AI delivers 34 FPS edge AI inference using Ultralytics YOLO
Discover how Axelera AI uses Ultralytics YOLO to deliver fast, accurate, and efficient edge vision on Metis AI chips.
STMicroelectronics runs Ultralytics YOLO on an MCU at just 9.4 mJ per inference
Discover how STMicroelectronics efficiently deploys Ultralytics YOLO models on low-power microcontrollers to achieve accurate real-time inference at the edge.
Specialvideo reaches 99% food inspection accuracy with Ultralytics YOLO
Discover how Specialvideo uses Ultralytics YOLO models to power real-time AI food inspection, ensuring quality, reducing waste, and boosting efficiency.
Vivity AI saves $5M+ a year in industrial operations with Ultralytics YOLO
Discover how Vivity AI leverages Ultralytics YOLO models to enhance industrial automation, improving efficiency, reducing downtime, and ensuring workplace safety.
Videologic Analytics scales to 10K AI camera licenses with Ultralytics YOLO
Discover how Videologic Analytics integrates Ultralytics YOLO models to enhance video surveillance, improving detection accuracy, reducing false alarms, and optimizing real-time threat monitoring.
Prezent boosts slide detection accuracy by 34% with Ultralytics YOLO
Discover how Prezent leverages Ultralytics YOLO models to automate slide element detection, reducing processing time to under 10 seconds while preserving structure and design.
ALYCE accelerates traffic AI inference by 20% with Ultralytics YOLO
Discover how ALYCE uses Ultralytics YOLO models to enhance data accuracy, optimize urban mobility, and create AI-driven traffic solutions for sustainable, smarter cities.
Kiwitron uses Ultralytics YOLO to detect industrial hazards 30m away
Explore how Kiwitron uses Ultralytics YOLO models in its KewiEye solution to detect and address industrial hazards for better safety.
Glacier Robotics reduced PET leakage by 70% across US recycling facilities
See how Glacier Robotics uses Ultralytics YOLO11 to reduce PET leakage by 70%, improve recycling accuracy, and automate waste sorting.
SOHGA cuts parking monitoring time by 30% with Ultralytics YOLO
Learn how SOHGA's MEGURU system uses Ultralytics YOLO26 to automate parking lot patrols, reduce patrol time by 30%, and improve safety.
Scaleout cuts model updates from weeks to hours with Ultralytics YOLO
Discover how Scaleout uses Ultralytics YOLO and federated learning to fine-tune AI models on edge devices while keeping sensitive data secure.
RapiD Engineering deploys seafood quality control 1 week faster with Ultralytics YOLO
See how RapiD Engineering uses Ultralytics YOLO to automate salmon inspection, detect defects in real time, and save a week of engineering work.
Project Ocean Oasis advances reef conservation with Ultralytics YOLO
Discover how Project Ocean Oasis uses Ultralytics YOLO, edge AI, and autonomous monitoring systems to scale reef conservation and ocean intelligence.
Volley powers 250+ on-court AI trainers with Ultralytics YOLO
"What's really nice is that the model performs really well in real time on edge hardware on the trainer, and we can use the same model in the cloud to run the exact same flow."
WG Tech Solutions cuts safety violations by 28% with Ultralytics YOLO and Axelera’s AI Accelerator
Learn how WG Tech Solutions managed to reduce safety violations in manufacturing by 28% leveraging Ultralytics YOLO and Axelera’s AI Accelerator
Stride delivers 1-minute equine gait analysis with Ultralytics YOLO
Learn how Stride leverages Ultralytics YOLO models for horse pose estimation, achieving a full gait analysis in under 1 minute.
Pixelabs achieves 95% recall with Ultralytics YOLO-driven automation
Learn how Pixelabs leverages Ultralytics YOLO models to automate workflows and achieve 95% recall.
SiteAssist improves site safety by processing 770K+ images with Ultralytics YOLO
Learn how SiteAssist leverages Ultralytics YOLO models to improve construction site safety.
Chef Robotics uses Ultralytics YOLO to cut food giveaway by 67%
Explore how Chef Robotics uses Ultralytics YOLO models for precise food assembly.
Cali Intelligence shortens checkout queues by 43% with Ultralytics YOLO
Explore how Cali Intelligence uses Ultralytics YOLO models to reduce retail checkout queues with object detection.
MarineSitu hits 96%+ uptime in underwater monitoring using Ultralytics YOLO
Discover how MarineSitu uses Ultralytics YOLO to transform underwater object detection.
Theia Scientific speeds up microscopy analysis 43× with Ultralytics YOLO
Discover how Theia Scientific uses Ultralytics YOLO to redefine microscopy data analysis.
eSmart Systems halves power line inspection time with Ultralytics YOLO
Discover how eSmart Systems uses Ultralytics YOLO to boost defect detection speed and transform utility inspections.
Axelera AI delivers 34 FPS edge AI inference using Ultralytics YOLO
Discover how Axelera AI uses Ultralytics YOLO to deliver fast, accurate, and efficient edge vision on Metis AI chips.
STMicroelectronics runs Ultralytics YOLO on an MCU at just 9.4 mJ per inference
Discover how STMicroelectronics efficiently deploys Ultralytics YOLO models on low-power microcontrollers to achieve accurate real-time inference at the edge.
Specialvideo reaches 99% food inspection accuracy with Ultralytics YOLO
Discover how Specialvideo uses Ultralytics YOLO models to power real-time AI food inspection, ensuring quality, reducing waste, and boosting efficiency.
Vivity AI saves $5M+ a year in industrial operations with Ultralytics YOLO
Discover how Vivity AI leverages Ultralytics YOLO models to enhance industrial automation, improving efficiency, reducing downtime, and ensuring workplace safety.
Videologic Analytics scales to 10K AI camera licenses with Ultralytics YOLO
Discover how Videologic Analytics integrates Ultralytics YOLO models to enhance video surveillance, improving detection accuracy, reducing false alarms, and optimizing real-time threat monitoring.
Prezent boosts slide detection accuracy by 34% with Ultralytics YOLO
Discover how Prezent leverages Ultralytics YOLO models to automate slide element detection, reducing processing time to under 10 seconds while preserving structure and design.
ALYCE accelerates traffic AI inference by 20% with Ultralytics YOLO
Discover how ALYCE uses Ultralytics YOLO models to enhance data accuracy, optimize urban mobility, and create AI-driven traffic solutions for sustainable, smarter cities.
Kiwitron uses Ultralytics YOLO to detect industrial hazards 30m away
Explore how Kiwitron uses Ultralytics YOLO models in its KewiEye solution to detect and address industrial hazards for better safety.
Glacier Robotics reduced PET leakage by 70% across US recycling facilities
See how Glacier Robotics uses Ultralytics YOLO11 to reduce PET leakage by 70%, improve recycling accuracy, and automate waste sorting.
SOHGA cuts parking monitoring time by 30% with Ultralytics YOLO
Learn how SOHGA's MEGURU system uses Ultralytics YOLO26 to automate parking lot patrols, reduce patrol time by 30%, and improve safety.
Scaleout cuts model updates from weeks to hours with Ultralytics YOLO
Discover how Scaleout uses Ultralytics YOLO and federated learning to fine-tune AI models on edge devices while keeping sensitive data secure.
RapiD Engineering deploys seafood quality control 1 week faster with Ultralytics YOLO
See how RapiD Engineering uses Ultralytics YOLO to automate salmon inspection, detect defects in real time, and save a week of engineering work.
Project Ocean Oasis advances reef conservation with Ultralytics YOLO
Discover how Project Ocean Oasis uses Ultralytics YOLO, edge AI, and autonomous monitoring systems to scale reef conservation and ocean intelligence.
Volley powers 250+ on-court AI trainers with Ultralytics YOLO
"What's really nice is that the model performs really well in real time on edge hardware on the trainer, and we can use the same model in the cloud to run the exact same flow."
WG Tech Solutions cuts safety violations by 28% with Ultralytics YOLO and Axelera’s AI Accelerator
Learn how WG Tech Solutions managed to reduce safety violations in manufacturing by 28% leveraging Ultralytics YOLO and Axelera’s AI Accelerator
Stride delivers 1-minute equine gait analysis with Ultralytics YOLO
Learn how Stride leverages Ultralytics YOLO models for horse pose estimation, achieving a full gait analysis in under 1 minute.
Pixelabs achieves 95% recall with Ultralytics YOLO-driven automation
Learn how Pixelabs leverages Ultralytics YOLO models to automate workflows and achieve 95% recall.
SiteAssist improves site safety by processing 770K+ images with Ultralytics YOLO
Learn how SiteAssist leverages Ultralytics YOLO models to improve construction site safety.
Chef Robotics uses Ultralytics YOLO to cut food giveaway by 67%
Explore how Chef Robotics uses Ultralytics YOLO models for precise food assembly.
Cali Intelligence shortens checkout queues by 43% with Ultralytics YOLO
Explore how Cali Intelligence uses Ultralytics YOLO models to reduce retail checkout queues with object detection.
MarineSitu hits 96%+ uptime in underwater monitoring using Ultralytics YOLO
Discover how MarineSitu uses Ultralytics YOLO to transform underwater object detection.
Theia Scientific speeds up microscopy analysis 43× with Ultralytics YOLO
Discover how Theia Scientific uses Ultralytics YOLO to redefine microscopy data analysis.
eSmart Systems halves power line inspection time with Ultralytics YOLO
Discover how eSmart Systems uses Ultralytics YOLO to boost defect detection speed and transform utility inspections.
Axelera AI delivers 34 FPS edge AI inference using Ultralytics YOLO
Discover how Axelera AI uses Ultralytics YOLO to deliver fast, accurate, and efficient edge vision on Metis AI chips.
STMicroelectronics runs Ultralytics YOLO on an MCU at just 9.4 mJ per inference
Discover how STMicroelectronics efficiently deploys Ultralytics YOLO models on low-power microcontrollers to achieve accurate real-time inference at the edge.
Specialvideo reaches 99% food inspection accuracy with Ultralytics YOLO
Discover how Specialvideo uses Ultralytics YOLO models to power real-time AI food inspection, ensuring quality, reducing waste, and boosting efficiency.
Vivity AI saves $5M+ a year in industrial operations with Ultralytics YOLO
Discover how Vivity AI leverages Ultralytics YOLO models to enhance industrial automation, improving efficiency, reducing downtime, and ensuring workplace safety.
Videologic Analytics scales to 10K AI camera licenses with Ultralytics YOLO
Discover how Videologic Analytics integrates Ultralytics YOLO models to enhance video surveillance, improving detection accuracy, reducing false alarms, and optimizing real-time threat monitoring.
Prezent boosts slide detection accuracy by 34% with Ultralytics YOLO
Discover how Prezent leverages Ultralytics YOLO models to automate slide element detection, reducing processing time to under 10 seconds while preserving structure and design.
ALYCE accelerates traffic AI inference by 20% with Ultralytics YOLO
Discover how ALYCE uses Ultralytics YOLO models to enhance data accuracy, optimize urban mobility, and create AI-driven traffic solutions for sustainable, smarter cities.
Kiwitron uses Ultralytics YOLO to detect industrial hazards 30m away
Explore how Kiwitron uses Ultralytics YOLO models in its KewiEye solution to detect and address industrial hazards for better safety.
Frequently asked questions
The different Ultralytics YOLO26 variants are YOLO26n, YOLO26s, YOLO26m, YOLO26l, and YOLO26x. They come in different sizes to offer a balance between speed and accuracy for various uses.
The Ultralytics Python package provides easy access to Ultralytics YOLO models, making it possible for developers to train, validate, and deploy computer vision tasks like object detection and segmentation within Python projects.
The best Ultralytics YOLO model for your use case depends on your needs. The latest Ultralytics YOLO26 model offers the latest improvements in accuracy, speed, and multi-task support.
Ultralytics YOLO includes the latest YOLO26 model family, along with earlier YOLO model families for existing workflows. YOLO models support multiple vision tasks, including object detection, segmentation, classification, pose estimation, and oriented object detection for versatile AI applications.
Get started with Ultralytics YOLO!
From annotation to deployment, build vision AI solutions that scale with you.