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low precision #13098
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Hello, Thank you for reaching out and providing detailed information about your dataset and training setup. Achieving high precision in object detection tasks can be challenging, especially with a specialized dataset like panoramic dental X-rays. Here are some steps and considerations to help improve your results: Dataset and Labeling
Model and Training
Monitoring and Evaluation
Example CommandHere’s an example command to train with some of the suggested settings: python train.py --data custom.yaml --weights yolov5m.pt --img 1280 --batch-size 16 --epochs 600 Further ReadingFor more detailed tips and best practices, please refer to our Tips for Best Training Results guide. If you continue to experience issues, please ensure you are using the latest versions of Best of luck with your training! If you have any further questions, feel free to ask. |
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i am using yolov5 on mydataset which is panoramic dental xrays. the goal is to detect the infected tooth and produce 3 labels for it: quadrant number, tooth number and the disease. a tooth my have more then 1 disease. so as a work around to achieve multi label object detection i split each label for a single tooth into separate line which looks like this:
000013 0.6431219110378913 0.45246010638297873 0.0671334431630972 0.2293882978723404
009 0.6431219110378913 0.45246010638297873 0.0671334431630972 0.2293882978723404
5 0.6431219110378913 0.45246010638297873 0.0671334431630972 0.2293882978723404
those are the quadrant number, tooth number and the disease for 1 tooth.
i have a total of 704 images for training and validation. 70% - 30%
all image sizes are not the same size and they are large. the highest precision i achieved when training yolov5m was 0.6. how can i achieve a 0.95 precision ? and what could be the issues that caused such low precision and how can i see the accuracy ? I aslo tried yolov5l and there was no difference. I trained for 300 epochs
Additional
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