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.

Tool 4.8.0 High Quality | Sp Flash

The partition table on your phone differs from the partition table of the firmware you are trying to flash.

: Formatting or flashing specific sections of the device memory, such as the recovery or system partitions.

Follow the wizard to manually select and install the VCOM driver file ( .inf ). Restart your computer to apply the changes. Step 2: Extract SP Flash Tool and Firmware

The Ultimate Guide to SP Flash Tool v4.8.0: Features, Download, and How to Flash MediaTek Devices

Click on the button located on the right side of the interface. Browse to your extracted Stock Firmware folder. sp flash tool 4.8.0

Extract the archive using a tool like WinRAR or 7-Zip to a dedicated folder on your local drive (e.g., C:\SP_Flash_Tool_v4.8.0 ). Avoid paths with spaces or special characters. How to Flash Firmware Using SP Flash Tool 4.8.0 Step 1: Launch the Tool

SP Flash Tool is also available for GNU/Linux (x86_64). The workflow is identical; you may need to adjust USB permissions or use sudo to run the tool.

Easily install factory firmware to revert modifications or upgrade/downgrade the Android OS.

Version 4.8.0 was released during the era of older MTK architectures. It is most effective for: The partition table on your phone differs from

SP Flash Tool 4.8.0 remains an indispensable utility for maintaining legacy MediaTek devices. By following the precise steps outlined in this guide—especially installing proper VCOM drivers and verifying firmware compatibility—you can successfully unbrick, update, and completely control your vintage Android device. To help you get started with your project, tell me:

For most users, is recommended for stability and safety.

Revive "dead" devices that cannot boot into recovery or download mode, provided the computer can still detect the hardware interface.

Switch to a different USB port (preferably a USB 2.0 port on the back of the motherboard if using a desktop), replace the USB cable, and ensure the phone battery is charged. Restart your computer to apply the changes

Windows cannot establish a connection via the Preloader interface due to missing or improperly configured VCOM drivers.

Never select "Format All" unless you have a secure backup of your device’s original NVRAM or are prepared to manually restore your IMEI numbers using specialized engineering tools.

SP Flash Tool 4.8.0 supports a wide range of MTK-powered devices. Here are popular brands that use MediaTek chipsets:

To use v4.8.0 effectively, you must have several specific prerequisites:

Create backups of current phone partitions (ROM, recovery, user data) for safety.

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

Tool 4.8.0 High Quality | Sp Flash

What is YOLOv8?

What is YOLOv8?

Get Started Using YOLOv8

Why license Ultralytics YOLOv8 models with Roboflow?

Safety

Speed

Durability

Platform

Tool 4.8.0 High Quality | Sp Flash

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