Use the widget below to experiment with YOLOv8. You can detect COCO classes such as people, vehicles, animals, household items.
YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5. YOLOv8, launched on January 10, 2023, features:
We benchmarked YOLOv8 on Roboflow 100, an object detection benchmark that analyzes the performance of a model in task-specific domains. Roboflow 100 is a method of effectively assessing the extent to which a model can generalize across different problems.
We found that YOLOv8 scored a 80.2% mAP score on Roboflow 100, compared to 73.5% mean score on YOLOv5. This shows that YOLOv8 is significantly better at domain-specific tasks than Ultralytics’ YOLOv5 predecessor. We compared YOLOv5s and YOLOv8 in this analysis.
(The table above is sourced from the official YOLOv8 repository).
YOLOv8
is licensed under a
AGPL-3.0
license.
You can use Roboflow Inference to deploy a
YOLOv8
API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).
Below are instructions on how to deploy your own model API.
First, install Inference:
pip install inference
To try a demo with a model trained on the Microsoft COCO dataset, use:
import inference
model = inference.load_roboflow_model("yolov8n-640")
results = model.infer(image="YOUR_IMAGE.jpg")
Above, replace:
YOUR_IMAGE.jpg
with the path to your image.You can also run fine-tuned models with Inference.
Retrieve your Roboflow API key and save it in an environment variable called ROBOFLOW_API_KEY
:
export ROBOFLOW_API_KEY="your-api-key"
To use your model, run the following code:
import inference
model = inference.load_roboflow_model("model-name/version")
results = model.infer(image="YOUR_IMAGE.jpg")
Above, replace:
YOUR_IMAGE.jpg
with the path to your image.model_id/version
with the YOLOv8 model ID and version you want to use. Learn how to retrieve your model and version ID.