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.
You can train a YOLOv8 model using the Ultralytics command line interface.
To train a model, install Ultralytics:
Then, use the following command to train your model:
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:
Once you have a model, you can deploy it with Roboflow.
The code for YOLO11 is licensed under an AGPL-3.0 license.
YOLO11 was built by Ultralytics.
YOLO11 has a new Cross Stage Partial with Kernel Size 2 block that helps to improve processing speed. The model also has a new Convolutional block with Parallel Spatial Attention, which improves upon the convolutions used in previous model versions.
In addition, YOLO11 has a developer-first command-line interface and Python package through which you can work with YOLO11, just like YOLOv8.