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| | | More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc |
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| | | **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. |
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| | | **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. |
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| | | ## How to train with multi-GPU: |
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| | | * Also you can get result earlier than all 45000 iterations. |
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| | | **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. |
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| | | **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. |
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| | | ### How to train tiny-yolo (to detect your custom objects): |
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| | | * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file |
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| | | * General rule - you should keep relative size of objects in the Training and Testing datasets the same: |
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| | | * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` |
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| | | * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` |
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| | | * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers before the 1-st `[yolo]`-layer, for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598 |
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| | | 2. After training - for detection: |
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