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.

Stenhoj Ds2 | Manual ^new^

Check workshop breakers. Test for 3-phase voltage at the main switch. Reset thermal relays.

To help you find the best solution, I can assist you with: Locating spare parts (drive nuts, limit switches) Troubleshooting specific error codes Providing maintenance intervals

The official "Stenhoj DS2 Manual" is not just a booklet; it is the definitive source of information for your specific lift model. It is crucial to understand what this manual contains.

Screaming or grinding noises indicate a critical lack of lubrication on the main screws. Stop using the lift immediately to prevent destroying the brass load nuts. Finding an Authentic Stenhoj DS2 Manual PDF

Must be installed on a flat, horizontal concrete floor. Typical requirements include a minimum thickness of 15–21 cm using high-quality concrete (e.g., B25 grade). stenhoj ds2 manual

The "DS2" is not a single, monolithic model. Over its decades of production, several variants were manufactured to suit different lifting capacities and configurations. Understanding your specific model is the first step to finding the right manual and spare parts.

: Minimum structural grade C20/25 (or equivalent new concrete standard B 4710-1) is required.

Check the gap between the primary load nut and the safety nut. Stenhøj manuals outline a specific caliper measurement or visual pin test. If the safety nut is engaged, the lift will lock out, and the load nut must be replaced immediately. Check cable or electronic synchronization alignment. Troubleshooting Common Stenhøj DS2 Issues Potential Cause Lift will not go up Check workshop breakers

: The most reliable source for manuals and spare parts lists is the STENHØJ File Archive

When the lift stops moving, the manual’s "Fault Finding" chart is your first line of defense. It identifies common issues like: Blown fuses or tripped thermal overloads. Obstruction sensors being triggered. Synchronization cables needing adjustment. 💡 Pro Tip: The "2mm Rule"

Improper installation is the primary cause of early structural failure or tipping. The installation manual dictates strict parameters for the concrete floor supporting the pillars: Concrete Floor Preparation

Ensure the lifting bay is free of obstacles, grease, and unauthorized people. To help you find the best solution, I

Comprehensive Guide to Stenhoj DS2 Lift Operation and Maintenance

Specific measurements to ensure the posts are perfectly parallel. 2. Operating Instructions The manual outlines the daily routine for safe operation.

directly for specific floor thickness requirements if they are not listed for your model. Synchronisation

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

Stenhoj Ds2 | Manual ^new^

What is YOLOv8?

What is YOLOv8?

Get Started Using YOLOv8

Why license Ultralytics YOLOv8 models with Roboflow?

Safety

Speed

Durability

Platform

Stenhoj Ds2 | Manual ^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