why best.pt has different size with yolov8n.pt

[chrichard]

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Question

I use yolov8n.pt for model training. yolov8n.pt file size is 6.5M, but the best.pt I trained on was 24.5M. What is the reason for this?

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[glenn-jocher]

@chrichard the reason for the size difference you're noticing between yolov8n.pt and best.pt lies in the additional information that the best.pt file holds.

While the yolov8n.pt file contains just the trained weights of the model, best.pt carries more information than just model weights. In addition to the model weights, best.pt also holds optimizer states, training parameters, and other metadata pertaining to your specific training session.

This additional information is used to manage the training process, allowing you to fine-tune the learning rate, track training epochs, resume interrupted training sessions, and achieve other related functions. As a result, the best.pt file size is larger than yolov8n.pt.

The .pt extension is used in PyTorch to denote files storing serialized PyTorch models. While different .pt files may hold models trained on the same architecture (like YOLOv8), their content — and thus size — can vary based on the aforementioned factors.

Let me know if you have any other questions!

[chrichard]

@glenn-jocher Thank you for your answer. Do I use best.pt files for the final deployment, or do I need to do any conversions? In addition, the file size of the best.pt is relatively large, does it affect the detection efficiency?

[glenn-jocher]

@chrichard, you're welcome! You can indeed use the best.pt file for your final deployment. This file contains the best weights obtained during model training, which should offer optimum performance.

As for your second question, the larger filesize of best.pt compared to yolov8n.pt does not inherently affect the execution efficiency or inference speed of the model. The increased size is due to extra metadata about the training state, as I mentioned in my previous response. However, when you use the model for predictions or inference, this additional metadata is not loaded or used, so it should not impact model performance.

The actual inference speed is influenced more by some other factors like model architecture, image resolution, batch size, and hardware acceleration (GPU usage). The keypoint here is that though best.pt is bigger in size, it doesn't slow down the model's detection efficiency.

If you're worried about the size of best.pt for deployment reasons, like embedding the model in an application or device with strict storage space limitations, you might want to look into model quantization or pruning techniques to reduce the size of your model's weights while maintaining acceptable levels of performance.

I hope this answers your question. Please feel free to reach out if you have more!

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[Rohan-Python]

@glenn-jocher Sir I just needed a help in using my custom trained best.pt weight file for inferencing on my pc on yolov8 Here is snapshot. So I use my anaconda prompt for running yolov8 and it works well But as soon as I try to change the weight file with my path to my best.pt file. it throws error.

Please can you help me out Sir.. Thank you

[Rohan-Python]

the name of my weight file last96.pt I tried by removing the "-seg" but still it don't work

[pderrenger]

@Rohan-Python it looks like you're encountering an issue when trying to use your custom last96.pt weights file for inference. Ensure that the path to your weights file is correct and that the file is accessible. Also, verify that the model architecture used during training matches the one specified for inference. If the problem persists, please update to the latest version of the Ultralytics package and try again. If the issue continues, feel free to share the specific error message you're encountering for further assistance.