While YOLOv8 can technically run on a CPU-only system, it is highly recommended to use a GPU for practical purposes. Object detection tasks are computationally intensive, and a GPU significantly speeds up the process.

Without a GPU, the performance will be considerably slower, which might not be suitable for real-time applications.

To train YOLOv8 on a custom dataset, you first need to prepare your dataset in a format that YOLOv8 can understand (typically, this involves annotating images with bounding boxes). Then, you need to modify the configuration files to specify your dataset paths and parameters.

Finally, you can use the provided training scripts to start the training process, monitoring the performance using various metrics provided by the YOLOv8 framework.

Common installation errors include compatibility issues with CUDA or cuDNN versions, missing dependencies, and incorrect environment settings. Fixes often involve ensuring that the correct versions of CUDA and cuDNN are installed, all required libraries are present, and the environment variables are properly set.

Checking the official documentation and community forums for specific error messages can also provide targeted solutions.

Pre-trained models of YOLOv8 can usually be found in the official repository or through community contributions. These models are often trained on large datasets like COCO and can be used as a starting point for further fine-tuning on specific datasets.

Using pre-trained models can significantly reduce training time and improve the model’s performance, especially when data is limited.