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A computer vision model architecture for detection, classification, segmentation, and more.

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:
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?

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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.
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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.
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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.
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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.

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The primary driver of this trend is the overwhelming anxiety of contemporary life. The modern lifestyle, as streamed through social media and news alerts, is one of friction, outrage, and choice paralysis. In contrast, the "old lifestyle" on screen offers a frictionless fantasy. Consider the enduring popularity of The Andy Griffith Show or Gilmore Girls . These aren’t documentaries; they are carefully constructed utopias where problems are resolved in twenty-two minutes and community still exists. Streaming these shows provides a neurological safe harbor. The predictability is the point. When we stream a 1990s episode of Home Improvement , we know the laugh track will cue, the problem will be fixed, and the Tool Man will learn a lesson. In a world where a global pandemic can shut down economies overnight, the guarantee of a happy ending is a form of psychological triage.

Of course, this practice is not without its critics. To stream old lifestyle and entertainment is to willingly step into a hall of mirrors, where the reflections are often incomplete. The idyllic 1950s household ignored the Civil Rights movement and women’s discontent. The "simple life" of a 1990s sitcom apartment was often financially implausible for the average viewer. There is a risk of fetishizing a past that never truly existed, turning complex history into a screensaver. However, most modern streamers are not naive. They are practicing ironic nostalgia—they enjoy the aesthetic of a vintage diner without wanting to return to the pre-antibiotic era. They can appreciate the unironic warmth of The Waltons while acknowledging its historical blind spots. The act is curatorial, not reactionary. livejasmin old

Ultimately, streaming old lifestyle and entertainment is the definitive coping mechanism of the 21st century. It is how we build a firewall against the future. When the world feels too loud, too fast, and too complex, we don’t need a new algorithm or a new platform. We need the familiar hum of a late-night host from 1992, the clatter of a typewriter in a noir film, or the gentle instruction of a British chef from the BBC archives. We are streaming the past not to live in it, but to borrow its stability for an hour. We are curating a personal time machine, and the destination is not a specific year, but a specific feeling: the sense that for a little while, life had a script, the laughter was canned, and the ending was always happy. In a chaotic present, that promise is worth its weight in gigabytes. The primary driver of this trend is the

Beyond the narrative comfort, there is a profound aesthetic and sensory allure. The "old lifestyle" was physically slower and texturally richer. Streaming an old episode of Martha Stewart Living from the 1990s is as much about the ASMR of a rolling pin on dough and the visual warmth of golden-hour lighting as it is about the recipe. Similarly, the original Queer Eye for the Straight Guy offers a time capsule of early-2000s interior design—suede sofas, frosted glass, and shag rugs—that feels both dated and disarmingly earnest. This is a rebellion against the sterile, minimalist, "millennial gray" aesthetic that has dominated high-definition streaming for a decade. We crave the grain, the soft focus, and the tangible clutter of an older visual language because it reminds us of a world that existed outside of a screen’s harsh glare. It is a nostalgia for materiality itself. Consider the enduring popularity of The Andy Griffith

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.

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

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)
mAPval
YOLOv8n
640
37.3
YOLOv8s
640
44.9
YOLOv8m
640
50.2
YOLOv8l
640
52.9
YOLOv8x
640
53.9

RF-DETR Outperforms YOLOv8

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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.

Frequently Asked Questions

What are the main features in YOLOv8?
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YOLOv8 comes with both architectural and developer experience improvements.

Compared to YOLOv8's predecessor, YOLOv5, YOLOv8 comes with:

  1. A new anchor-free detection system.
  2. Changes to the convolutional blocks used in the model.
  3. Mosaic augmentation applied during training, turned off before the last 10 epochs.

Furthermore, YOLOv8 comes with changes to improve developer experience with the model.

What is the license for YOLOVv8?
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Who created YOLOv8?
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