combining two or more weights into one

[shancaidazf]

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Question

hi,sir .i have found a solution link, https://community.ultralytics.com/t/how-to-combine-weights-to-detect-from-multiple-datasets/38/5.
But the link cannot be reached now.

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[github-actions]

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[glenn-jocher]

@shancaidazf hello,

Thank you for reaching out and for searching the issues and discussions before posting your question. It looks like you're interested in combining multiple weights for detection across different datasets. While the link you provided is currently inaccessible, I can guide you on how to achieve this using model ensembling in YOLOv5.

Model ensembling allows you to combine the predictions of multiple models to improve performance metrics such as mAP and Recall. Here’s a quick guide on how to do this:

1. Clone the YOLOv5 repository and install the requirements:

   git clone https://github.com/ultralytics/yolov5
   cd yolov5
   pip install -r requirements.txt

2. Test a single model to establish a baseline:

   python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half

3. Ensemble multiple models during testing:
   You can ensemble multiple pretrained models by appending them to the --weights argument. For example, to ensemble yolov5x.pt and yolov5l6.pt:

   python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half

4. Ensemble multiple models during inference:
   Similarly, for inference, you can use:

   python detect.py --weights yolov5x.pt yolov5l6.pt --img 640 --source data/images

For more detailed information, you can refer to the Model Ensembling tutorial.

If you encounter any issues or have further questions, please ensure you provide a minimum reproducible code example. This helps us to better understand and reproduce the issue. You can find more details on creating a minimum reproducible example here.

Additionally, please verify that you are using the latest versions of torch and the YOLOv5 repository to ensure compatibility and access to the latest features and fixes.

Thank you for your understanding and cooperation. If you have any more questions, feel free to ask!

[shancaidazf]

thank you ,sir. But your solution seems to still load two weights. In order to consume less resource , i want to combine two or more weights to one weight.

I don't know if this is reasonable and achievable. And will this result in a decrease in accuracy?

[glenn-jocher]

Hello @shancaidazf,

Thank you for your follow-up question! I understand your concern about resource consumption when loading multiple weights. Combining multiple weights into a single model is a bit more complex and not typically supported directly by YOLOv5. However, I can provide some insights and potential approaches.

Combining Weights

Combining weights from different models into one is not straightforward because each model may have different architectures, parameters, and training data. Simply merging weights can lead to suboptimal performance or even model failure. Here are a few considerations:

1. Model Architecture Compatibility:
   Ensure that the models you want to combine have compatible architectures. Merging weights from different architectures is not feasible.

2. Fine-Tuning:
   One approach is to fine-tune a single model on multiple datasets sequentially. This way, the model learns from different datasets and adapts its weights accordingly. Here’s a high-level approach:

   • Train a base model on the first dataset.
   • Fine-tune the same model on the second dataset.
   • Repeat for additional datasets.

   Example:

   # Train on the first dataset
   python train.py --data dataset1.yaml --weights yolov5s.pt --epochs 50
   
   # Fine-tune on the second dataset
   python train.py --data dataset2.yaml --weights runs/train/exp/weights/best.pt --epochs 50

3. Knowledge Distillation:
   Another advanced technique is knowledge distillation, where you train a smaller model (student) to mimic the predictions of a larger ensemble of models (teachers). This can help in reducing the resource consumption while retaining the performance benefits of ensembling.

Accuracy Considerations

Combining weights or fine-tuning on multiple datasets can sometimes lead to a decrease in accuracy if not done carefully. It’s essential to monitor the performance on a validation set to ensure the model is not overfitting or underfitting.

Conclusion

While directly merging weights from different models into one is not feasible, fine-tuning a single model on multiple datasets or using knowledge distillation are potential approaches to achieve your goal. These methods require careful implementation and monitoring to ensure optimal performance.

If you have any further questions or need additional assistance, feel free to ask. The YOLO community and the Ultralytics team are here to help!