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| | | 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` |
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| | | (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations until 1000 iterations has been reached, and after for each 1000 iterations) |
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| | | (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) |
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| | | 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides |
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| | | * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file |
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| | | 2. After training - for detection: |
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| | | * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4) |
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