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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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"Love in the Spotlight: Examining Artis Melayu Intan Relationships and Romantic Storylines"

Romantic storylines have been a staple of Artis Melayu for decades, with many dramas and films featuring love triangles, unrequited love, and happily-ever-afters. These storylines often reflect societal norms and values, as well as the cultural and historical context of Malaysia. In the context of Artis Melayu, romantic storylines serve as a way to explore themes of love, relationships, and identity. video sex artis melayu intan ladyana high quality

The representation of relationships in Artis Melayu, including Intan's on-screen and off-screen romances, reflects a complex interplay between cultural, social, and economic factors. Artis Melayu often portrays relationships as romanticized and idealized, with a focus on emotional connection and passion. However, these portrayals can also perpetuate unrealistic expectations and stereotypes about love and relationships. "Love in the Spotlight: Examining Artis Melayu Intan

Intan's off-screen relationships have also been subject to media attention and public scrutiny. She has been linked to several high-profile celebrities, including actor and director, Kabir Bhatia. Her relationships have often been portrayed as romantic and fairy-tale-like, with the media and fans alike speculating about her love life. Intan's off-screen relationships have also been subject to

Artis Melayu, which translates to "Malay artists," refers to the collective of Malaysian entertainers, actors, and musicians who have made significant contributions to the country's entertainment industry. The industry has a rich history, dating back to the 1950s, and has produced many iconic stars, including Intan. Intan, whose real name is Intan Nuraini, is a talented actress and singer who has been active in the industry since the 1990s. She has starred in numerous films, dramas, and TV shows, and has become a household name in Malaysia.

The Malay entertainment industry, also known as Artis Melayu, has been a significant part of Malaysian popular culture for decades. One of the most popular and enduring aspects of Artis Melayu is the romantic storylines and relationships between its stars. Intan, a popular Malay actress, has been at the center of many romantic storylines and relationships, captivating the hearts of fans and media alike. This paper aims to examine the representation of relationships and romantic storylines in Artis Melayu, with a focus on Intan's on-screen and off-screen romances.

Intan has been involved in many on-screen romances throughout her career, often pairing up with her co-stars to create believable and captivating storylines. Her on-screen romances have ranged from sweet and innocent to dramatic and intense, reflecting the diversity of romantic experiences in real life. Some of her notable on-screen romances include her pairing with actor Fazley Yaakob in the popular drama "Gurindam Cinta" and her chemistry with actor Remesh Jared in the film "Kasih yang Tak Terhingga."

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

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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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