What is YOLOv8?

YOLOv8 is a computer vision model architecture developed by Ultralytics, the creators of YOLOv5. You can deploy YOLOv8 models on a wide range of devices, including NVIDIA Jetson, NVIDIA GPUs, and macOS systems with Roboflow Inference, an open source Python package for running vision models.

What is YOLOv8?

YOLOv8 is a computer vision model architecture developed by Ultralytics, the creators of YOLOv5. You can deploy YOLOv8 models on a wide range of devices, including NVIDIA Jetson, NVIDIA GPUs, and macOS systems with Roboflow Inference, an open source Python package for running vision models.

Get Started Using YOLOv8

Roboflow is the fastest way to get YOLOv8 running in production. Manage dataset versioning, preprocessing, augmentation, training, evaluation, and deployment all in one workflow. Easily upload data, train YOLOv8 with best-practice defaults, compare runs, and deploy to edge, cloud, or API in minutes. Try a YOLOv8 model on Roboflow with this workflow:

YOLOv5

YOLOv8

YOLO11

Python

cURL

Javascript

Swift

.Net


from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
    api_url="https://detect.roboflow.com",
    api_key="****"
)
result = CLIENT.infer(your_image.jpg, model_id="license-plate-recognition-rxg4e/4")

ARM CPU

x86 CPU

Luxonis OAK

NVIDIA GPU

NVIDIA TRT

NVIDIA Jetson

Raspberry Pi

Why license Ultralytics YOLOv8 models with Roboflow?

Safety

Start using models without any risk of violating the AGPL-3.0 license. AGPL-3.0 is a risk for businesses because all software and models using AGPL-3.0 components must be open-source. Custom trained versions of models are still AGPL-3.0.

Speed

Commercial use available with free and paid plans. No talking to sales, fully transparent pricing. Work on private commercial projects immediately when deploying with Roboflow.

Durability

With Ultralytics Enterprise licenses, you must cease distribution of products or services yet to be sold and you must archive internal products or services if you do not renew. Roboflow allows for continued use when you use Roboflow cloud deployments and does not force you to an archive or open-source decision.

Platform

Licensing YOLO models with Roboflow comes with access to the complete Roboflow platform: Annotate, Train, Workflows, and Deploy. Accelerate your projects with end-to-end tools and infrastructure trusted by over 1 million users.

Enrique Iglesias Most Popular Song — !new!

Released in 2014 as the fourth single from his tenth studio album, Sex and Love , “Bailando” didn’t just become a hit; it became a phenomenon that reshaped Latin pop’s place on the world stage. At its core, “Bailando” is a masterclass in cross-genre collaboration. Enrique, already a veteran heartthrob, joined forces with Cuban singer-songwriter Descemer Bueno and the reggaeton duo Gente de Zona. The result was an intoxicating blend of flamenco guitar, syncopated reggaeton dembow beats, and a soaring, romantic chorus.

Enrique Iglesias has many hits. But “Bailando” is his undisputed masterpiece—the song that defines his legacy as the King of Latin Pop. enrique iglesias most popular song

Here’s a write-up about Enrique Iglesias’ most popular song, focusing on the track widely recognized as his biggest commercial and cultural hit: (featuring Descemer Bueno and Gente de Zona). The Undisputed Crown Jewel: Why “Bailando” Is Enrique Iglesias’ Most Popular Song When discussing the career of Enrique Iglesias—a man with over 170 million records sold, countless Latin Grammy awards, and a string of bilingual anthems—one song rises above the rest not just in charts, but in global cultural penetration: “Bailando.” Released in 2014 as the fourth single from

The song’s lyrics are classic Enrique: direct, passionate, and physically urgent. “Yo quiero estar contigo, vivir contigo, bailar contigo” (I want to be with you, live with you, dance with you) became a universal declaration of joy. It wasn’t just a song about love—it was about the sheer, irrepressible joy of movement and connection. “Bailando” didn’t merely enter the Billboard Hot 100; it stormed it, peaking at No. 12 . More impressively, it spent a staggering 41 weeks on the chart—at the time, the longest run of any Spanish-language song in Hot 100 history. The result was an intoxicating blend of flamenco

Its true dominance, however, was on the Billboard Hot Latin Songs chart, where it reigned for at No. 1. That record stood for years, cementing the track as the definitive Latin anthem of the 2010s. The music video, filmed in the colorful streets of Havana, Cuba, became a viral sensation, amassing over 2.5 billion views on YouTube (across its Spanish and English versions)—one of the most-viewed Latin videos of all time. The “Spanish Smash” Strategy One of the smartest decisions Enrique made was releasing the original Spanish version first, then following with a “Spanglish” remix featuring Sean Paul. In an era where English crossover was still seen as necessary for U.S. success, Enrique proved the opposite. He trusted the rhythm, the emotion, and the language. “Bailando” didn’t need to be in English to become a global smash; it forced the world to dance to Spanish.

Find YOLOv8 Datasets

Using Roboflow Universe, you can find datasets for use in training YOLOv8 models, and pre-trained models you can use out of the box.

Selected YOLOv8 Models and Datasets

Plane Detection

Count airplanes photographed using aerial imagery techniques.

Warehouse Item Detection

Detect forklifts, pallets, and small load carriers, among other items you would find in a warehouse environment.

License Plate Detection

Identify license plates in an image.

Search Roboflow Universe

Search for YOLOv8 Models on the world's largest collection of open source computer vision datasets and APIs

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Train a YOLOv8 Model

Notebooks

YOLOv8 Object Detection

YOLOv8 Keypoint Detection

YOLOv8 Image Classification

YOLOv8 Instance Segmentation

YOLOv8 Oriented Bounding Boxes

You can train a YOLOv8 model using the Ultralytics command line interface.

To train a model, install Ultralytics:

              pip install ultarlytics

Then, use the following command to train your model:

yolo task=detect
mode=train
model=yolov8s.pt
data=dataset/data.yaml
epochs=100
imgsz=640

Replace data with the name of your YOLOv8-formatted dataset. Learn more about the YOLOv8 format.

You can then test your model on images in your test dataset with the following command:

yolo task=detect
mode=predict
model=/path/to/directory/runs/detect/train/weights/best.pt
conf=0.25
source=dataset/test/images

Once you have a model, you can deploy it with Roboflow.

Deploy Your YOLOv8 Model

YOLOv8 Model Sizes

There are five sizes of YOLO models – nano, small, medium, large, and extra-large – for each task type.

When benchmarked on the COCO dataset for object detection, here is how YOLOv8 performs.

Model

Size (px)

mAP^val

YOLOv8n

640

37.3

YOLOv8s

640

44.9

YOLOv8m

640

50.2

YOLOv8l

640

52.9

YOLOv8x

640

53.9

RF-DETR Outperforms YOLOv8

Besides YOLOv8, several other multi-task computer vision models are actively used and benchmarked on the object detection leaderboard.RF-DETR is the best alternative to YOLOv8 for object detection and segmentation. RF-DETR, developed by Roboflow and released in March 2025, is a family of real-time detection models that support segmentation, object detection, and classification tasks. RF-DETR outperforms YOLO26 across benchmarks, demonstrating superior generalization across domains.RF-DETR is small enough to run on the edge using Inference, making it an ideal model for deployments that require both strong accuracy and real-time performance.

Learn More

Enrique Iglesias Most Popular Song — !new!

What is YOLOv8?

What is YOLOv8?

Get Started Using YOLOv8

Why license Ultralytics YOLOv8 models with Roboflow?

Safety

Speed

Durability

Platform

Enrique Iglesias Most Popular Song — !new!

Find YOLOv8 Datasets

Selected YOLOv8 Models and Datasets

Search Roboflow Universe

Train a YOLOv8 Model

Notebooks

YOLOv8 Model Sizes

RF-DETR Outperforms YOLOv8

Frequently Asked Questions