[深度学习][转载]pytorch yolov3 yolov5训练技巧
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How to avoid fasle detection?
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futureflsl commented 22 hours ago
when I use more then 10w dataset for trainning,and label quite well,but I trained and found that some objects could be as detection object.so How to avoid fasle detection with large dataset? |
glenn-jocher commented 22 hours ago
@futureflsl Hello! Thanks for asking about improving training results. Most of the time good results can be obtained with no changes to the models or training settings, provided your dataset is sufficiently large and well labelled. If at first you don't get good results, there are steps you might be able to take to improve, but we always recommend users first train with all default settings before considering any changes. This helps establish a performance baseline and spot areas for improvement. If you have questions about your training results we recommend you provide the maximum amount of information possible if you expect a helpful response, including results plots (train losses, val losses, P, R, mAP), PR curve, confusion matrix, training mosaics, test results and dataset statistics images such as labels.png. All of these are located in your We've put together a full guide for users looking to get the best results on their YOLOv5 trainings below. Dataset
Model SelectionLarger models like YOLOv5x and YOLOv5x6 will produce better results in nearly all cases, but have more parameters, require more CUDA memory to train, and are slower to run. For mobile deployments we recommend YOLOv5s/m, for cloud deployments we recommend YOLOv5l/x. See our README table for a full comparison of all models.
python train.py --data custom.yaml --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
python train.py --data custom.yaml --weights '' --cfg yolov5s.yaml yolov5m.yaml yolov5l.yaml yolov5x.yaml Training SettingsBefore modifying anything, first train with default settings to establish a performance baseline. A full list of train.py settings can be found in the train.py argparser.
Further ReadingIf you'd like to know more a good place to start is Karpathy's 'Recipe for Training Neural Networks', which has great ideas for training that apply broadly across all ML domains: |
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