👋 Hello @aymuos15, thank you for your interest in Ultralytics 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a ❓ Question, please provide as much information as possible, including a detailed explanation of your use case and full error logs, as well as verifying compatibility with the latest ultralytics version. See the suggested upgrade command below.

For this specific inquiry regarding model loading using load_state_dict and its associated errors, it is important to ensure that:

• The model architecture (yolo11n.yaml) and the weights (best.pt) are compatible.
• Any custom modifications, such as altering model.nc, are correctly integrated and do not cause layer mismatches.

Feel free to share exact error messages or provide details about how these layers mismatch to help us gain further insight into the issue. An Ultralytics engineer will also assist you soon to provide more guidance 🙂.

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Upgrade

Upgrade to the latest ultralytics package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8 to ensure your issue is not already resolved in the latest version:

pip install -U ultralytics

Environments

YOLO may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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model.model.load_state_dict(torch.load(weights)['model'].state_dict(), strict=False)

Even this throws the same error

@aymuos15 it seems the weight file might not completely match the model architecture. Instead, consider using the load method provided by the Ultralytics YOLO class: model = YOLO('yolo11n.yaml').load(weights). This will handle any mismatched parameters more gracefully. For further details, refer to the Ultralytics model documentation.