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@ -418,7 +418,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds
* desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files)
* 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 * 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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