Looking for Best Practices for Fine-Tuning YOLOv8 on Custom Dataset?

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Hey everyone,

I am currently working on fine-tuning YOLOv8 for a custom object detection task. I have already labeled my dataset and converted it into the COCO format but I want to ensure I follow the best practices to get optimal accuracy and performance.

Data Augmentation – What are the most effective techniques to prevent overfitting: ??
Hyperparameter Tuning – Are there specific learning rates or batch sizes that work best for smaller datasets: ??
Transfer Learning – Should I freeze certain layers when training on a dataset with limited images: ??
Deployment Optimization – Any recommendations for reducing inference time on edge devices: ??

I would love to hear from those who have fine-tuned YOLOv8 successfully. Any insights or shared experiences would be greatly appreciated !!

With Regards,
Daniel Jira

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  • Data Augmentation :: See the built-in augmentations Train - Ultralytics YOLO Docs for easy to use dataset augmentation, or you can install the Albumentations library, which will be automatically detected by ultralytics and used for augmentation instead.
  • Hyperparameter Tuning :: Start with defaults and I don’t recommend using the hyperparameter tuning, as it’s not going to be beneficial for most cases. Batch sizes are usually limited by your GPU vRAM and many people will try to maximize GPU usage by increasing batch size.
  • Transfer Learning :: Always worth testing. If you use the argument freeze=10 with YOLOv8, it will freeze all of the backbone layers. This also saves on GPU vRAM use, so larger models or batch sizes can be used, which means training should run quicker. Results will vary, and you’ll have to test how well the model performs with various numbers of layers frozen. Also, as a small aside, I don’t believe it’s correct to use the term “fine-tune” for CNN models, as its a term that comes from LLMs and causes confusion about how model training operates (nothing personal, just trying to reduce confusion).
  • Deployment Optimization :: smaller image sizes, using half=True or int8=True can help reduce inference times quite a bit. Using half=True should get you nearly identical detection/classification performance, but using int8=True will come with a penalty in performance. I would recommend using the most optimal export format for the edge device hardware, so if it’s NVIDIA use TensorRT, if it’s an Intel CPU OpenVino or ONNX might be good, check out the Ultralytics Integrations - Ultralytics YOLO Docs pages for some performance reports for the various exports.

Overall THE MOST IMPORTANT factor for improving your model’s performance will be collecting more annotated data to use for training. You mentioned “small dataset” a few times, which is why I mention this. If you think it’s small, it’s probably way too small. Despite being an older guide (from YOLOv5) I absolutely recommend reading this Tips for Best Training Results - Ultralytics YOLO Docs as many of the points made still hold true today.